From db8f316f0ba5d842c0f031771208a856cc183a17 Mon Sep 17 00:00:00 2001
From: learnsomethingtoday
 <56990829+learnsomethingtoday@users.noreply.github.com>
Date: Sat, 2 Oct 2021 23:16:01 +0530
Subject: [PATCH 001/232] Create numberofislands.cpp

---
 C++/numberofislands.cpp | 76 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100644 C++/numberofislands.cpp

diff --git a/C++/numberofislands.cpp b/C++/numberofislands.cpp
new file mode 100644
index 0000000..5a638cf
--- /dev/null
+++ b/C++/numberofislands.cpp
@@ -0,0 +1,76 @@
+#include<bits/stdc++.h>
+using namespace std;
+int a[1000][1000];
+int visited[1000][1000];
+vector<pair<int,int>> v;
+int dist[1000][1000];
+void bfs(int m,int n)
+{
+	for(auto i:v)
+	{
+		int time = 0;
+		int x = i.first;
+		int y = i.second;
+		dist[x][y] = 0;
+		queue<pair<int,int>> q;
+		q.push(make_pair(x,y));
+		while(!q.empty())
+		{
+			int t = q.front().first;
+			int u = q.front().second;
+			q.pop();
+			//cout<<t<<u<<" ";
+			if(t+1<m and a[t+1][u]==1){a[t+1][u]=2;dist[t+1][u] = dist[t][u] + 1;q.push(make_pair(t+1,u));}
+			if(u+1<m and a[t][u+1]==1){a[t][u+1]=2;dist[t][u+1] = dist[t][u] + 1;q.push(make_pair(t,u+1));}
+			if(t-1>=0 and a[t-1][u]==1){a[t-1][u]=2;dist[t-1][u] = dist[t][u] + 1;q.push(make_pair(t-1,u));}
+			if(u-1>=0 and a[t][u-1]==1){a[t][u-1]=2;dist[t][u-1] = dist[t][u] + 1;q.push(make_pair(t,u-1));}
+			//cout<<'a'<<q.front().first<<q.front().second;
+		} 
+		
+	}
+	int fl=0;
+	int maxi = 0;
+	for(int i=0;i<m;i++)
+	{
+		for(int j=0;j<n;j++)
+		{
+			cout<<a[i][j];
+			if(a[i][j]==1){fl=1;break;}
+			maxi = max(maxi,dist[i][j]);
+		}
+	}	
+	if(fl)cout<<-1;
+	else cout<<maxi;
+}
+int main()
+{
+	int m,n;
+	cin>>m>>n;
+	for(int i=0;i<m;i++)
+	{
+		for(int j=0;j<n;j++)
+			{
+				cin>>a[i][j];
+			}
+	}
+	for(int i=0;i<m;i++)
+	{
+		for(int j=0;j<n;j++)
+			{
+				if(a[i][j]==2)
+					{
+						v.push_back(make_pair(i,j));
+					}
+			}
+	}
+	bfs(m,n);
+	// for(int i=0;i<m;i++)
+	// {
+	// 	for(int j=0;j<n;j++)
+	// 		{
+	// 			if(a[i][j]==1)cnt=-1;
+	// 		}
+	// }
+
+	
+}

From 52b275228dfac16fe5f4949389d4b22f2de78aab Mon Sep 17 00:00:00 2001
From: Chetan Thakral <77783033+chetan-2002@users.noreply.github.com>
Date: Tue, 5 Oct 2021 11:33:20 +0530
Subject: [PATCH 002/232] Updated readme.md file of data structures folder.

There was a small spelling mistake. I had resolved that.
---
 Data Structures/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Data Structures/readme.md b/Data Structures/readme.md
index b02dbc7..fb16adf 100644
--- a/Data Structures/readme.md	
+++ b/Data Structures/readme.md	
@@ -1,4 +1,4 @@
-# Data Sctuctures
+# Data Structures
 
 **Data Structures** is a named location that can be used to store and organize data. And, an algorithm is a collection of steps to solve a particular problem. Learning data structures and algorithms allow us to write efficient and optimized computer programs.
 

From fe73cfaa3126bc8d5678cb066f048433dce83e2f Mon Sep 17 00:00:00 2001
From: M-Rafay <66524287+M-Rafay@users.noreply.github.com>
Date: Tue, 5 Oct 2021 16:06:53 +0500
Subject: [PATCH 003/232] Create BFS.py

BFS (Breadth First Search)
---
 Python/Searching/BFS.py | 64 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 64 insertions(+)
 create mode 100644 Python/Searching/BFS.py

diff --git a/Python/Searching/BFS.py b/Python/Searching/BFS.py
new file mode 100644
index 0000000..e4296a2
--- /dev/null
+++ b/Python/Searching/BFS.py
@@ -0,0 +1,64 @@
+# Python3 Program to print BFS traversal
+# from a given source vertex. BFS(int s)
+# traverses vertices reachable from s.
+from collections import defaultdict
+
+# This class represents a directed graph
+# using adjacency list representation
+class Graph:
+
+	# Constructor
+	def __init__(self):
+
+		# default dictionary to store graph
+		self.graph = defaultdict(list)
+
+	# function to add an edge to graph
+	def addEdge(self,u,v):
+		self.graph[u].append(v)
+
+	# Function to print a BFS of graph
+	def BFS(self, s):
+
+		# Mark all the vertices as not visited
+		visited = [False] * (max(self.graph) + 1)
+
+		# Create a queue for BFS
+		queue = []
+
+		# Mark the source node as
+		# visited and enqueue it
+		queue.append(s)
+		visited[s] = True
+
+		while queue:
+
+			# Dequeue a vertex from
+			# queue and print it
+			s = queue.pop(0)
+			print (s, end = " ")
+
+			# Get all adjacent vertices of the
+			# dequeued vertex s. If a adjacent
+			# has not been visited, then mark it
+			# visited and enqueue it
+			for i in self.graph[s]:
+				if visited[i] == False:
+					queue.append(i)
+					visited[i] = True
+
+# Driver code
+
+# Create a graph given in
+# the above diagram
+g = Graph()
+g.addEdge(0, 1)
+g.addEdge(0, 2)
+g.addEdge(1, 2)
+g.addEdge(2, 0)
+g.addEdge(2, 3)
+g.addEdge(3, 3)
+
+print ("Following is Breadth First Traversal"
+				" (starting from vertex 2)")
+g.BFS(2)

From fd0b1da5c1fd7642ba74989f04af5cb21d34c242 Mon Sep 17 00:00:00 2001
From: M-Rafay <66524287+M-Rafay@users.noreply.github.com>
Date: Tue, 5 Oct 2021 16:09:04 +0500
Subject: [PATCH 004/232] Create BFS.cs

Breadth First Search
---
 C#/Searching/BFS.cs | 99 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 99 insertions(+)
 create mode 100644 C#/Searching/BFS.cs

diff --git a/C#/Searching/BFS.cs b/C#/Searching/BFS.cs
new file mode 100644
index 0000000..977104e
--- /dev/null
+++ b/C#/Searching/BFS.cs
@@ -0,0 +1,99 @@
+// C# program to print BFS traversal
+// from a given source vertex.
+// BFS(int s) traverses vertices
+// reachable from s.
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+
+// This class represents a directed
+// graph using adjacency list
+// representation
+class Graph{
+	
+// No. of vertices	
+private int _V;
+
+//Adjacency Lists
+LinkedList<int>[] _adj;
+
+public Graph(int V)
+{
+	_adj = new LinkedList<int>[V];
+	for(int i = 0; i < _adj.Length; i++)
+	{
+		_adj[i] = new LinkedList<int>();
+	}
+	_V = V;
+}
+
+// Function to add an edge into the graph
+public void AddEdge(int v, int w)
+{		
+	_adj[v].AddLast(w);
+
+}
+
+// Prints BFS traversal from a given source s
+public void BFS(int s)
+{
+	
+	// Mark all the vertices as not
+	// visited(By default set as false)
+	bool[] visited = new bool[_V];
+	for(int i = 0; i < _V; i++)
+		visited[i] = false;
+	
+	// Create a queue for BFS
+	LinkedList<int> queue = new LinkedList<int>();
+	
+	// Mark the current node as
+	// visited and enqueue it
+	visited[s] = true;
+	queue.AddLast(s);		
+
+	while(queue.Any())
+	{
+		
+		// Dequeue a vertex from queue
+		// and print it
+		s = queue.First();
+		Console.Write(s + " " );
+		queue.RemoveFirst();
+		
+		// Get all adjacent vertices of the
+		// dequeued vertex s. If a adjacent
+		// has not been visited, then mark it
+		// visited and enqueue it
+		LinkedList<int> list = _adj[s];
+
+		foreach (var val in list)			
+		{
+			if (!visited[val])
+			{
+				visited[val] = true;
+				queue.AddLast(val);
+			}
+		}
+	}
+}
+
+// Driver code
+static void Main(string[] args)
+{
+	Graph g = new Graph(4);
+	
+	g.AddEdge(0, 1);
+	g.AddEdge(0, 2);
+	g.AddEdge(1, 2);
+	g.AddEdge(2, 0);
+	g.AddEdge(2, 3);
+	g.AddEdge(3, 3);
+	
+	Console.Write("Following is Breadth First " +
+				"Traversal(starting from " +
+				"vertex 2)\n");
+	g.BFS(2);
+}
+}

From b7b7f8e37e3f66af728350ba258b7b436973a115 Mon Sep 17 00:00:00 2001
From: M-Rafay <66524287+M-Rafay@users.noreply.github.com>
Date: Tue, 5 Oct 2021 16:10:51 +0500
Subject: [PATCH 005/232] Create BFS.java

Breadth First Search
---
 Java/Search/BFS.java | 81 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 81 insertions(+)
 create mode 100644 Java/Search/BFS.java

diff --git a/Java/Search/BFS.java b/Java/Search/BFS.java
new file mode 100644
index 0000000..2dd69e3
--- /dev/null
+++ b/Java/Search/BFS.java
@@ -0,0 +1,81 @@
+// Java program to print BFS traversal from a given source vertex.
+// BFS(int s) traverses vertices reachable from s.
+import java.io.*;
+import java.util.*;
+
+// This class represents a directed graph using adjacency list
+// representation
+class Graph
+{
+	private int V; // No. of vertices
+	private LinkedList<Integer> adj[]; //Adjacency Lists
+
+	// Constructor
+	Graph(int v)
+	{
+		V = v;
+		adj = new LinkedList[v];
+		for (int i=0; i<v; ++i)
+			adj[i] = new LinkedList();
+	}
+
+	// Function to add an edge into the graph
+	void addEdge(int v,int w)
+	{
+		adj[v].add(w);
+	}
+
+	// prints BFS traversal from a given source s
+	void BFS(int s)
+	{
+		// Mark all the vertices as not visited(By default
+		// set as false)
+		boolean visited[] = new boolean[V];
+
+		// Create a queue for BFS
+		LinkedList<Integer> queue = new LinkedList<Integer>();
+
+		// Mark the current node as visited and enqueue it
+		visited[s]=true;
+		queue.add(s);
+
+		while (queue.size() != 0)
+		{
+			// Dequeue a vertex from queue and print it
+			s = queue.poll();
+			System.out.print(s+" ");
+
+			// Get all adjacent vertices of the dequeued vertex s
+			// If a adjacent has not been visited, then mark it
+			// visited and enqueue it
+			Iterator<Integer> i = adj[s].listIterator();
+			while (i.hasNext())
+			{
+				int n = i.next();
+				if (!visited[n])
+				{
+					visited[n] = true;
+					queue.add(n);
+				}
+			}
+		}
+	}
+
+	// Driver method to
+	public static void main(String args[])
+	{
+		Graph g = new Graph(4);
+
+		g.addEdge(0, 1);
+		g.addEdge(0, 2);
+		g.addEdge(1, 2);
+		g.addEdge(2, 0);
+		g.addEdge(2, 3);
+		g.addEdge(3, 3);
+
+		System.out.println("Following is Breadth First Traversal "+
+						"(starting from vertex 2)");
+
+		g.BFS(2);
+	}
+}

From 184e3289adfd581b3f93c9fd49ba7ebf75b645d6 Mon Sep 17 00:00:00 2001
From: M-Rafay <66524287+M-Rafay@users.noreply.github.com>
Date: Tue, 5 Oct 2021 16:13:10 +0500
Subject: [PATCH 006/232] Create DFS.py

Depth First Search
---
 Python/Searching/DFS.py | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 Python/Searching/DFS.py

diff --git a/Python/Searching/DFS.py b/Python/Searching/DFS.py
new file mode 100644
index 0000000..0e8b712
--- /dev/null
+++ b/Python/Searching/DFS.py
@@ -0,0 +1,22 @@
+# Using a Python dictionary to act as an adjacency list
+graph = {
+  '5' : ['3','7'],
+  '3' : ['2', '4'],
+  '7' : ['8'],
+  '2' : [],
+  '4' : ['8'],
+  '8' : []
+}
+
+visited = set() # Set to keep track of visited nodes of graph.
+
+def dfs(visited, graph, node):  #function for dfs 
+    if node not in visited:
+        print (node)
+        visited.add(node)
+        for neighbour in graph[node]:
+            dfs(visited, graph, neighbour)
+
+# Driver Code
+print("Following is the Depth-First Search")
+dfs(visited, graph, '5')

From 455cbd06d6339b0c18d181da13561edb60980e39 Mon Sep 17 00:00:00 2001
From: Sameer-aftab <53132294+Sameer-aftab@users.noreply.github.com>
Date: Tue, 5 Oct 2021 18:19:19 +0500
Subject: [PATCH 007/232] Create Data loader for Health Monitorization

Data Loader for Health monitorization after collecting data using Health Monitorization code (Available in C++ Directory) you can just get an SD card out from a circuit and  then you can use the above Data loader Program to convert raw data into meaning full DATA that can be easily fed to ML model
---
 Python/Data loader for Health Monitorization | 44 ++++++++++++++++++++
 1 file changed, 44 insertions(+)
 create mode 100644 Python/Data loader for Health Monitorization

diff --git a/Python/Data loader for Health Monitorization b/Python/Data loader for Health Monitorization
new file mode 100644
index 0000000..ab03735
--- /dev/null
+++ b/Python/Data loader for Health Monitorization	
@@ -0,0 +1,44 @@
+# Data loader for Health monitarization
+
+
+import pandas as pd    # here we have imported Pandas Library 
+
+file_to_load = "E:/DATA - Copy.txt"   # here we are having a .txt and we want to Extract data from a txt file split that data and create a CSV for our ML model
+
+file_to_save = "E:/DATA Processed.csv"  # Location where processed Data will be saved
+
+DF = pd.read_csv( file_to_load, index_col=None )    # class to read 
+
+bpm = DF['bpm']                             # Label Declaration 
+temp_f = DF['temp_f']                       # Label Declaration 
+temp_c = DF['temp_c']                       # Label Declaration 
+temp_date = DF['date']                      # Label Declaration 
+
+
+Date = []                                   # Array Decleration
+Time = []                                   # Array Decleration
+BPM = []                                    # Array Decleration
+Temp_f = []                                 # Array Decleration
+Temp_c = []
+
+for aa in range( len(temp_date) ):
+    
+    temp = temp_date[aa]
+    
+    try:
+
+        Date.append( temp.split("\t")[1].split("T")[0] )
+        Time.append( temp.split("\t")[1].split("T")[1] )
+        
+        BPM.append( bpm[aa] )
+        Temp_f.append( temp_f[aa] )
+        Temp_c.append( temp_c[aa] )
+                
+    except:
+            
+        pass
+    
+    
+DF = pd.DataFrame( {"Date":Date, "Time":Time, "BPM":BPM, "Temp_f":Temp_f, "Temp_c":Temp_c}, index=None )     # creating a CSV file 
+DF.to_csv( file_to_save )                                                                                    # Saving data to CSV file 
+

From dc56228a046abce10b9f916cef7c50e0fe3a0bd8 Mon Sep 17 00:00:00 2001
From: Chetan Thakral <77783033+chetan-2002@users.noreply.github.com>
Date: Tue, 5 Oct 2021 19:06:22 +0530
Subject: [PATCH 008/232] Create Insertion_Sort.cpp

Added the code for the Insertion Sort algorithm in C++.
---
 .../Sorting/C++/Insertion_Sort.cpp            | 47 +++++++++++++++++++
 1 file changed, 47 insertions(+)
 create mode 100644 Data Structures/Sorting/C++/Insertion_Sort.cpp

diff --git a/Data Structures/Sorting/C++/Insertion_Sort.cpp b/Data Structures/Sorting/C++/Insertion_Sort.cpp
new file mode 100644
index 0000000..3eed8a3
--- /dev/null
+++ b/Data Structures/Sorting/C++/Insertion_Sort.cpp	
@@ -0,0 +1,47 @@
+//This file explains everything about Insetion sort
+//Intutuion : The basic idea behind Insetion sort is basically the array is virtually split into a sorted and an unsorted part. Values from the unsorted part are picked and placed at the correct position in the sorted array.
+
+//C++ Code
+#include <bits/stdc++.h>
+using namespace std;
+//Insertion sort function
+void insertionSort(int arr[], int n)
+{
+    int i, j, key;
+    for (int i = 1; i < n; i++)
+    {
+        //Below code is for picking an element in the unsorted array and placing at its correct position.
+        key = arr[i];
+        j = i - 1;
+        while (j >= 0 && arr[j] > key)
+        {
+            arr[j + 1] = arr[j];
+            j--;
+        }
+        arr[j + 1] = key;
+    }
+}
+//Driver function :
+int main()
+{
+    int n;
+    cin >> n;
+    int arr[n];
+    //Taking input form the user
+    for (int i = 0; i < n; i++)
+    {
+        cin >> arr[i];
+    }
+
+    //calling the insertion sort for sorting the array in-place
+    insertionSort(arr, n);
+
+    //printing the array
+    for (int i = 0; i < n; i++)
+    {
+        cout << arr[i] << " ";
+    }
+}
+
+//Time complexity : O(N^2) ,here n is the number of elements in the array
+//Auxiliary Space : O(1)
\ No newline at end of file

From fb5f404bd4077c1be7a783389b1aed43e0761131 Mon Sep 17 00:00:00 2001
From: QuratAin <quratulainshoro1@outlook.com>
Date: Wed, 6 Oct 2021 11:23:31 +0500
Subject: [PATCH 009/232] added a new code

---
 Javascript/currentDateandTime.js | 37 ++++++++++++++++++++++++++++++++
 Javascript/main.html             | 13 +++++++++++
 learn2code                       | 37 ++++++++++++++++++++++++++++++++
 3 files changed, 87 insertions(+)
 create mode 100644 Javascript/currentDateandTime.js
 create mode 100644 Javascript/main.html
 create mode 100644 learn2code

diff --git a/Javascript/currentDateandTime.js b/Javascript/currentDateandTime.js
new file mode 100644
index 0000000..8d5b027
--- /dev/null
+++ b/Javascript/currentDateandTime.js
@@ -0,0 +1,37 @@
+// DISPLAYING CURRENT DAY AND TIME          
+var now = new Date();
+//console.log(now);
+var day = now.getDay();
+//console.log(day);
+    switch (day) {
+        case 0:
+            day = "Sunday";
+            break;
+        case 1:
+            day = "Monday";
+            break;
+        case 2:
+            day = "Tuesday";
+            break;
+        case 3:
+            day = "Wednesday";
+            break;
+        case 4:
+            day = "Thursday";
+            break;
+        case 5:
+            day = "Friday";
+            break;
+        case 6:
+            day = "Saturday";
+            break;
+        default:
+            day = "Invalid";
+            break;
+            }
+
+    var hr = now.getHours();
+    var min = now.getMinutes();
+    var sec = now.getSeconds();
+    document.write("Today is : " + day);
+    document.write("<br>Current Time :" + hr + " A.M : " + min + " : " + sec);
diff --git a/Javascript/main.html b/Javascript/main.html
new file mode 100644
index 0000000..10a09d5
--- /dev/null
+++ b/Javascript/main.html
@@ -0,0 +1,13 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <script src="currentDateandTime.js"></script>
+    <title>Qurat</title>
+</head>
+<body>
+    
+</body>
+</html>
\ No newline at end of file
diff --git a/learn2code b/learn2code
new file mode 100644
index 0000000..3cb3f3b
--- /dev/null
+++ b/learn2code
@@ -0,0 +1,37 @@
+// DISPLAYING CURRENT DATE AND TIME          
+var now = new Date();
+//console.log(now);
+var day = now.getDay();
+//console.log(day);
+    switch (day) {
+        case 0:
+            day = "Sunday";
+            break;
+        case 1:
+            day = "Monday";
+            break;
+        case 2:
+            day = "Tuesday";
+            break;
+        case 3:
+            day = "Wednesday";
+            break;
+        case 4:
+            day = "Thursday";
+            break;
+        case 5:
+            day = "Friday";
+            break;
+        case 6:
+            day = "Saturday";
+            break;
+        default:
+            day = "Invalid";
+            break;
+            }
+
+    var hr = now.getHours();
+    var min = now.getMinutes();
+    var sec = now.getSeconds();
+    document.write("Today is : " + day);
+    document.write("<br>Current Time :" + hr + " A.M : " + min + " : " + sec);

From dda55a63161c38ec1a52ef0a112e51db0aa273d5 Mon Sep 17 00:00:00 2001
From: QuratAin <quratulainshoro1@outlook.com>
Date: Wed, 6 Oct 2021 11:24:35 +0500
Subject: [PATCH 010/232] Added new code

---
 Javascript/{currentDateandTime.js => currentDayandTime.js} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Javascript/{currentDateandTime.js => currentDayandTime.js} (100%)

diff --git a/Javascript/currentDateandTime.js b/Javascript/currentDayandTime.js
similarity index 100%
rename from Javascript/currentDateandTime.js
rename to Javascript/currentDayandTime.js

From b3368f8a09dfd6f17531ff5d5629f29231ea94f3 Mon Sep 17 00:00:00 2001
From: Qurat Ul Ain Shoro <65081454+QuratAin@users.noreply.github.com>
Date: Wed, 6 Oct 2021 11:32:11 +0500
Subject: [PATCH 011/232] Delete learn2code

---
 learn2code | 37 -------------------------------------
 1 file changed, 37 deletions(-)
 delete mode 100644 learn2code

diff --git a/learn2code b/learn2code
deleted file mode 100644
index 3cb3f3b..0000000
--- a/learn2code
+++ /dev/null
@@ -1,37 +0,0 @@
-// DISPLAYING CURRENT DATE AND TIME          
-var now = new Date();
-//console.log(now);
-var day = now.getDay();
-//console.log(day);
-    switch (day) {
-        case 0:
-            day = "Sunday";
-            break;
-        case 1:
-            day = "Monday";
-            break;
-        case 2:
-            day = "Tuesday";
-            break;
-        case 3:
-            day = "Wednesday";
-            break;
-        case 4:
-            day = "Thursday";
-            break;
-        case 5:
-            day = "Friday";
-            break;
-        case 6:
-            day = "Saturday";
-            break;
-        default:
-            day = "Invalid";
-            break;
-            }
-
-    var hr = now.getHours();
-    var min = now.getMinutes();
-    var sec = now.getSeconds();
-    document.write("Today is : " + day);
-    document.write("<br>Current Time :" + hr + " A.M : " + min + " : " + sec);

From aecde5683dc2094d7c1d46ce1f7f0bbcf590c865 Mon Sep 17 00:00:00 2001
From: rzmk <mueez25@gmail.com>
Date: Wed, 6 Oct 2021 03:21:05 -0400
Subject: [PATCH 012/232] Add mergeTwoSortedArrays JS file

---
 Javascript/mergeTwoSortedArrays.js | 45 ++++++++++++++++++++++++++++++
 1 file changed, 45 insertions(+)
 create mode 100644 Javascript/mergeTwoSortedArrays.js

diff --git a/Javascript/mergeTwoSortedArrays.js b/Javascript/mergeTwoSortedArrays.js
new file mode 100644
index 0000000..e9fff35
--- /dev/null
+++ b/Javascript/mergeTwoSortedArrays.js
@@ -0,0 +1,45 @@
+// Function to merge two sorted (increasing) arrays of numbers
+
+function mergeTwoSortedArrays(arr1, arr2) {
+	// Setup pointers (indexes for traversing arrays), and mergedArray array
+	let ptr1 = 0
+	let ptr2 = 0
+	let mergedArray = []
+
+	// Check for empty input
+	if (arr1.length === 0 && arr2.length === 0) return mergedArray // if both arrays empty return empty mergedArray
+	if (arr1.length === 0) return arr2 // if arr1 is empty return arr2
+	if (arr2.length === 0) return arr1 // if arr2 is empty return arr1
+
+	// While either pointer is not equal to length of their array:
+	while (ptr1 < arr1.length && ptr2 < arr2.length) {
+		// Add arr1[ptr1] to mergedArray array and increase ptr1 by 1
+		if (arr1[ptr1] <= arr2[ptr2]) {
+			mergedArray.push(arr1[ptr1])
+			ptr1++
+		}
+		// Add arr2[ptr2] to mergedArray array and increase ptr2 by 1
+		else if (arr1[ptr1] > arr2[ptr2]) {
+			mergedArray.push(arr2[ptr2])
+			ptr2++
+		}
+	}
+
+	// If ptr1 hasn't traversed all of arr1, push these remaining arr1 values to mergedArray
+	while (ptr1 < arr1.length) {
+		mergedArray.push(arr1[ptr1])
+		ptr1++
+	}
+	// If ptr2 hasn't traversed all of arr2, push these remaining arr2 values to mergedArray
+	while (ptr2 < arr2.length) {
+		mergedArray.push(arr2[ptr2])
+		ptr2++
+	}
+
+	return mergedArray
+}
+
+// Driver code to test above
+const array1 = [0, 3, 4, 31]
+const array2 = [4, 6, 30]
+console.log(mergeTwoSortedArrays(array1, array2)) // [0, 3, 4, 4, 6, 30, 31]

From b3cec7ec315b516ae3c6d2fa478307717989a520 Mon Sep 17 00:00:00 2001
From: Sameer Aftab <53132294+Sameer-aftab@users.noreply.github.com>
Date: Thu, 7 Oct 2021 01:41:05 +0500
Subject: [PATCH 013/232] Create Decision Tree Example

A simple Example of a Decision tree to classify between Apples and Oranges we are using only two features
1. Weight  ( Heavy/Light )
2. Bumpy/Soft
---
 Machine Learning/Decision Tree Example | 12 ++++++++++++
 1 file changed, 12 insertions(+)
 create mode 100644 Machine Learning/Decision Tree Example

diff --git a/Machine Learning/Decision Tree Example b/Machine Learning/Decision Tree Example
new file mode 100644
index 0000000..64993d8
--- /dev/null
+++ b/Machine Learning/Decision Tree Example	
@@ -0,0 +1,12 @@
+# Problem Statement For suppose if i want to classify between Apple and oranges so could i ?
+# i need to define or i need to clasify that weather its bumpy or soft ?
+# so i will be using Supervised Learning where things are predictable 
+
+from sklearn import tree                # here i have just imported  a decision tree library from its parent package Sklearn also known as scikit learn 
+features = [[140, 1], [130, 1], [150, 0], [170, 0] ]   # here we are inputing 2 features both we compare in integer for making code easy 140,130,150,170 its siply weight of fruit and 0 or 1 shows weather its bumpy or soft 
+                                                      # or in simple we can say that its Training Data 
+labels  = [0, 0, 1, 1]                # here are the labels for the above classsifier 
+clf = tree.DecisionTreeClassifier()   # at this step i am invoking a pre trained classifier that is decision tree classifier 
+clf = clf.fit(features, labels)       # here i am fiting data that our classsifier provided according to our Labels and features 
+print clf.pridict ( [[150, 0]])       # here we input data that tou our model that tell me this is orange or apple  answer will be a bool value as we have dectared 0 or 1 
+                                      # if it hits 1 so yes it's heavy and bumpy thats what our classifier pridicts 

From 729175a6ceb36422558f1e933f863dfa85da119a Mon Sep 17 00:00:00 2001
From: PallaviPareek <50631982+PallaviPareek@users.noreply.github.com>
Date: Thu, 7 Oct 2021 18:44:38 +0530
Subject: [PATCH 014/232] Machine Learning

---
 Machine Learning/Machine Learning.pdf | Bin 0 -> 3532498 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Machine Learning/Machine Learning.pdf

diff --git a/Machine Learning/Machine Learning.pdf b/Machine Learning/Machine Learning.pdf
new file mode 100644
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zy}<9>R`9y_`PJ5<Xdefhwriiq@3_8%ea?k%JDtzJCD8q~F7P|I)!nY`|FSL4OK8a7
zakYhc`mOD1yPnS+xgmOf|9y|naIvI*#npDb+w!X|AV&jh+FH;#+q%v9-A8dxvc`Y$
zTmADt{fGbbzy0Gs{q<k{>wo>X|I>f^zyI-XdP4sB+kgLG{>wlAP0Ie}-~a7@|DXTk
zfBBo9OaIsZ__zPdzwVLaKm6Oj|9Sjf`$_-q@1Fng_pk5Yzu$lN-~IjH{lmX|{QaMQ
z_iz5cBmURF`8WUOum9@*_~-xdPyhUX{nJ1G$G`ros{?xm_}72+um06P{15-`um9@5
zw)XS<^<Vw}^auX!|M*Y;_<#P*fBU=s_J98O|M(yOr+@z6`|rQ}eCJ=^n{JJw|Mu7a
EKQe@0O8@`>

literal 0
HcmV?d00001


From 9197ce6cd8d94665bb980e8c3d9815f3076f0c3b Mon Sep 17 00:00:00 2001
From: Mahnoor Naveed <mahnoornaveed74@gmail.com>
Date: Fri, 8 Oct 2021 09:58:44 +0500
Subject: [PATCH 015/232] resolved issue add-code-for-javascript

---
 Javascript/ternaryOperators.js | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 Javascript/ternaryOperators.js

diff --git a/Javascript/ternaryOperators.js b/Javascript/ternaryOperators.js
new file mode 100644
index 0000000..791b567
--- /dev/null
+++ b/Javascript/ternaryOperators.js
@@ -0,0 +1,23 @@
+// Ternary Operator in Javascript
+
+// Ternary operators are conditional operators and takes three operands.
+// condition to check with ?
+// an expression to execute if the condition is true with :
+// and last expression if the given condition is false
+
+let a = 4;
+let b = 3;
+
+a == b ? console.log("a and b are equal") : console.log("a and b are not equal");
+// OUTPUT
+// a and b are not equal
+
+
+// For Multiple Conditions 
+a > b
+  ? console.log("a is greater")
+  : a < b
+  ? console.log("b is greater")
+  : console.log(" a and b are equal");
+// OUTPUT
+// a is greater
\ No newline at end of file

From 7eb38ab58434605af10c54f5805fb200b02888e0 Mon Sep 17 00:00:00 2001
From: Rafay Saif <57329000+rafay840@users.noreply.github.com>
Date: Fri, 8 Oct 2021 10:09:11 +0500
Subject: [PATCH 016/232] Create JavaScript program to determine a given year
 is a leap year i

---
 ...pt program to determine a given year is a leap year i | 9 +++++++++
 1 file changed, 9 insertions(+)
 create mode 100644 Javascript/JavaScript program to determine a given year is a leap year i

diff --git a/Javascript/JavaScript program to determine a given year is a leap year i b/Javascript/JavaScript program to determine a given year is a leap year i
new file mode 100644
index 0000000..aee189d
--- /dev/null
+++ b/Javascript/JavaScript program to determine a given year is a leap year i	
@@ -0,0 +1,9 @@
+function leapyear(year)
+{
+return (year % 100 === 0) ? (year % 400 === 0) : (year % 4 === 0);
+}
+console.log(leapyear(2016));
+console.log(leapyear(2000));
+console.log(leapyear(1700));
+console.log(leapyear(1800));
+console.log(leapyear(100));

From 09a1e4ca552c0d116715bde8e0d2b209cf4a4813 Mon Sep 17 00:00:00 2001
From: Rabia-Rajput <rabiarajput0300@gmail.com>
Date: Sat, 9 Oct 2021 16:27:33 +0500
Subject: [PATCH 017/232] Added conditional struture program

---
 Python/Searching/conditional_structure.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Python/Searching/conditional_structure.py

diff --git a/Python/Searching/conditional_structure.py b/Python/Searching/conditional_structure.py
new file mode 100644
index 0000000..e69de29

From 6e16e28f5bfd3fcfe847bc92da5922b8b1d66bd5 Mon Sep 17 00:00:00 2001
From: Rabia-Rajput <rabiarajput0300@gmail.com>
Date: Sat, 9 Oct 2021 17:01:47 +0500
Subject: [PATCH 018/232] Added conditional structure program in python

---
 Python/Searching/conditional_structure.py | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)

diff --git a/Python/Searching/conditional_structure.py b/Python/Searching/conditional_structure.py
index e69de29..727bec9 100644
--- a/Python/Searching/conditional_structure.py
+++ b/Python/Searching/conditional_structure.py
@@ -0,0 +1,23 @@
+eng,Islam,Maths=-5,-6,-8
+TotalMarks=300
+percent= (eng+Islam+Maths)/TotalMarks*100
+if percent<100 and percent>80:
+    print("Grade A+")
+elif percent<80 and percent>70:
+    print("Grade A")
+elif percent<70 and percent>60:
+    print("Grade B")
+elif percent<60 and percent>50:
+    print("Grade C")
+elif percent<50 and percent>40:
+    print("Grade D")
+elif percent<40 and percent>30:
+    print("Grade E")
+elif percent>0 and percent<30:
+    print(" Fail" )
+elif percent>100:
+    print("Percentage can't be greater than 100!")
+elif percent<0:
+    print("Percentage can't be negative!")
+else:
+    print("Something wrong!")
\ No newline at end of file

From b301c8a7f67bcea2b04db8da2a03c0202964a9f9 Mon Sep 17 00:00:00 2001
From: Rafay Saif <57329000+rafay840@users.noreply.github.com>
Date: Sun, 10 Oct 2021 11:59:04 +0500
Subject: [PATCH 019/232] Update JavaScript program to determine a given year
 is a leap year i

---
 ... program to determine a given year is a leap year i | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/Javascript/JavaScript program to determine a given year is a leap year i b/Javascript/JavaScript program to determine a given year is a leap year i
index aee189d..8bd557c 100644
--- a/Javascript/JavaScript program to determine a given year is a leap year i	
+++ b/Javascript/JavaScript program to determine a given year is a leap year i	
@@ -1,8 +1,12 @@
-function leapyear(year)
+function leapyear(year)    //function starts from here
 {
-return (year % 100 === 0) ? (year % 400 === 0) : (year % 4 === 0);
+//A year is leap year if following conditions are satisfied: 
+//Year is multiple of 400. Year is multiple of 4 and not multiple of 100.
+//three conditions to find out the leap year 
+return (year % 100 === 0) ? (year % 400 === 0) : (year % 4 === 0);                                                                    
 }
-console.log(leapyear(2016));
+//Checking Results and logging it to the console
+console.log(leapyear(2016)); 
 console.log(leapyear(2000));
 console.log(leapyear(1700));
 console.log(leapyear(1800));

From e1c7a2951a53ffc8e2cf9b6dc9594b877aaf8d8b Mon Sep 17 00:00:00 2001
From: Muhammad Hassan <60064591+Mhassanbughio@users.noreply.github.com>
Date: Sun, 10 Oct 2021 20:41:16 +0500
Subject: [PATCH 020/232] Update helloworld.dart

---
 Dart/helloworld.dart | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/Dart/helloworld.dart b/Dart/helloworld.dart
index cf959f8..8df9ad6 100644
--- a/Dart/helloworld.dart
+++ b/Dart/helloworld.dart
@@ -1,3 +1,11 @@
 void main() {
+  var String = "MLSA MUET SZAB";
+  var String1 = "mlsa muet szab";
+  print(String.toLowerCase());
+  print(String.toUpperCase());
+  print(String.trim());
+  print(String.length);
+  print(String.isNotEmpty);
+  print(String.compareTo(String1));
   print('Hello World');
 }

From e5d2e485a3b3ddfbb8ba131d6bc7ad54deb794fe Mon Sep 17 00:00:00 2001
From: Sameer Aftab <53132294+Sameer-aftab@users.noreply.github.com>
Date: Sun, 10 Oct 2021 23:23:18 +0500
Subject: [PATCH 021/232] Shuffle a Deck of Card

Let's Play some card
a python code that can be used to randomly shuffle cards
---
 Python/Shuffle a Deck of Card | 15 +++++++++++++++
 1 file changed, 15 insertions(+)
 create mode 100644 Python/Shuffle a Deck of Card

diff --git a/Python/Shuffle a Deck of Card b/Python/Shuffle a Deck of Card
new file mode 100644
index 0000000..604983b
--- /dev/null
+++ b/Python/Shuffle a Deck of Card	
@@ -0,0 +1,15 @@
+# Python program to shuffle a deck of card
+
+# importing modules
+import itertools, random
+
+# make a deck of cards
+deck = list(itertools.product(range(1,14),['Spade','Heart','Diamond','Club']))
+
+# shuffle the cards
+random.shuffle(deck)
+
+# draw five cards
+print("You got:")
+for i in range(5):
+   print(deck[i][0], "of", deck[i][1])

From a483f288980d33b471ecb12af0c8435860bd87e2 Mon Sep 17 00:00:00 2001
From: Shoaib Khalid <shoaibkakil@gmail.com>
Date: Tue, 12 Oct 2021 08:15:35 +0500
Subject: [PATCH 022/232] Create Weirdalgo.cppp

---
 C++/Weirdalgo.cppp | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)
 create mode 100644 C++/Weirdalgo.cppp

diff --git a/C++/Weirdalgo.cppp b/C++/Weirdalgo.cppp
new file mode 100644
index 0000000..15ce5fa
--- /dev/null
+++ b/C++/Weirdalgo.cppp
@@ -0,0 +1,18 @@
+#include <iostream>
+using namespace std;
+
+int main()
+{
+	long long int n;
+	cin >> n;
+	cout << n << " ";
+	while (n > 1)
+	{
+		if (n & 1)
+			n = (3 * n) + 1;
+		else
+			n >>= 1;
+		cout << n << " ";
+	}
+	return 0;
+}

From 9274d9a3dbee49518f4459f863b86cf2dfedbbc5 Mon Sep 17 00:00:00 2001
From: javeriatabassum145 <javeria.tabassum145@gmail.com>
Date: Tue, 12 Oct 2021 10:05:52 +0500
Subject: [PATCH 023/232] Adding Marksheet generator python program

---
 Python/Marksheet.py | 37 +++++++++++++++++++++++++++++++++++++
 1 file changed, 37 insertions(+)
 create mode 100644 Python/Marksheet.py

diff --git a/Python/Marksheet.py b/Python/Marksheet.py
new file mode 100644
index 0000000..47a11b8
--- /dev/null
+++ b/Python/Marksheet.py
@@ -0,0 +1,37 @@
+Name=input("Enter your name:")
+Roll_no=input("Enter your roll no")
+Total_marks=425
+English=int(input("Enter you English marks out of 75:"))
+Pak_Studies=int(input("Enter you Pakistan Studies marks out of 75:"))
+Computer=int(input("Enter you Computer marks out of 100:"))
+Chemistry=int(input("Enter you Chemistry marks out of 100:"))
+Sindhi=int(input("Enter you Sindhi marks out of 75:"))
+Total_obtained_marks=(English+Pak_Studies+Computer+Chemistry+Sindhi)
+Percentage=((Total_obtained_marks/Total_marks)*100)
+print("*********************************************************** \n")
+print("\t \t  \" MARKSHEET SSC PART 1 \"  \n")
+print("Name        :",Name)
+print("Roll number : ",Roll_no)
+print("Subjects          Total Obtained ")
+print("English Marks     "," 75  ",English)
+print("Pak Studies Marks "," 75  ",Pak_Studies)
+print("Computer Marks    "," 100 ",Computer)
+print("Chemistry Marks   "," 100 ",Chemistry)
+print("Sindhi Marks      "," 75  ",Sindhi)
+print("------------------------------------")
+print("                  ",Total_marks," ",Total_obtained_marks)
+print("\n")
+if Percentage <= 100 and Percentage >= 80 :
+    print("\t\" Your percentage is",Percentage,"and you got A+ Grade \"")
+elif Percentage < 80 and Percentage >= 70 :
+    print("\t\" Your percentage is",Percentage,"and you got A Grade \"")
+elif Percentage < 70 and Percentage >= 60 : 
+    print("\t\" Your percentage is",Percentage,"and you got B Grade \"")
+elif Percentage < 60 and Percentage >= 50 : 
+    print("\t\" Your percentage is",Percentage,"and you got C Grade \"")
+elif Percentage < 50 and Percentage >= 40 : 
+    print("\t\" Your percentage is",Percentage,"and you got D Grade \"")
+else:
+    print("\t\" You are fail \"")
+
+print("\n ************************************************************")
\ No newline at end of file

From 22faeb89424cb188f29b62e27f785a4d397cad0f Mon Sep 17 00:00:00 2001
From: javeriatabassum145 <javeria.tabassum145@gmail.com>
Date: Tue, 12 Oct 2021 10:10:28 +0500
Subject: [PATCH 024/232] Adding Minimum Maximum Number generator python
 program

---
 .../Min_Max_generator_from_random_numbers.py  | 21 +++++++++++++++++++
 1 file changed, 21 insertions(+)
 create mode 100644 Python/Min_Max_generator_from_random_numbers.py

diff --git a/Python/Min_Max_generator_from_random_numbers.py b/Python/Min_Max_generator_from_random_numbers.py
new file mode 100644
index 0000000..e048b89
--- /dev/null
+++ b/Python/Min_Max_generator_from_random_numbers.py
@@ -0,0 +1,21 @@
+import random
+#CREATING_RANDOM_LIST
+random_list=[]
+for i in range(100):
+    random_list.append(random.randint(1,100))
+print(random_list)
+#MINIMUM_NUMBER
+minimum_no=[]
+minimum_no.append(min(random_list))
+print("Minimum value in the list is : " , minimum_no)
+#MAXIMUM_NUMBER
+maximum_no=[]
+maximum_no.append(max(random_list))
+print("Maximum value in the list is : " , maximum_no)
+#INDEX
+print("Index of minimum value in list is : " , random_list.index(min(random_list)))
+print("Index of maximum value in list is : " , random_list.index(max(random_list)))
+#MEAN_OF_VALUES
+mean_of_randomlist=sum(random_list)/len(random_list)
+print("Mean of values of list : " , mean_of_randomlist)
+

From 1a2c946e89ba9d4e689c4a72d9d6b7cda63ddc3a Mon Sep 17 00:00:00 2001
From: Javeria Tabassum <50498182+javeriatabassum145@users.noreply.github.com>
Date: Wed, 13 Oct 2021 11:44:31 +0500
Subject: [PATCH 025/232] Update Min_Max_generator_from_random_numbers.py

---
 .../Min_Max_generator_from_random_numbers.py  | 21 +++++++++++++++++--
 1 file changed, 19 insertions(+), 2 deletions(-)

diff --git a/Python/Min_Max_generator_from_random_numbers.py b/Python/Min_Max_generator_from_random_numbers.py
index e048b89..4946848 100644
--- a/Python/Min_Max_generator_from_random_numbers.py
+++ b/Python/Min_Max_generator_from_random_numbers.py
@@ -4,18 +4,35 @@
 for i in range(100):
     random_list.append(random.randint(1,100))
 print(random_list)
+#Output :
+#[96, 32, 32, 10, 97, 45, 54, 41, 34, 99, 41, 97, 79, 72, 83, 8, 1, 91, 86, 83, 7, 99, 23, 59, 83, 70, 22, 58, 30, 46, 25, 45, 93, 46, 57, 19, 81, 77, 41, 11, 80, 87, 14, 42, 21, 28, 54, 27, 47, 33, 8, 73, 74, 15, 75, 73, 28, 57, 69, 33, 53, 40, 31, 18, 49, 64, 38, 74, 20, 48, 61, 37, 54, 97, 1, 62, 29, 18, 14, 84, 83, 73, 98, 84, 58, 65, 8, 74, 72, 43, 20, 50, 14, 34, 27, 50, 87, 21, 29, 52]
+
 #MINIMUM_NUMBER
 minimum_no=[]
 minimum_no.append(min(random_list))
 print("Minimum value in the list is : " , minimum_no)
+#Output :
+#Minimum value in the list is :  [1]
+
 #MAXIMUM_NUMBER
 maximum_no=[]
 maximum_no.append(max(random_list))
 print("Maximum value in the list is : " , maximum_no)
-#INDEX
+#Output :
+#Maximum value in the list is :  [99]
+    
+#INDEX OF MIN VALUE
 print("Index of minimum value in list is : " , random_list.index(min(random_list)))
+#Output :
+#Index of minimum value in list is :  16
+
+#INDEX OF MAX VALUE
 print("Index of maximum value in list is : " , random_list.index(max(random_list)))
+#Output :
+#Index of maximum value in list is :  9
+
 #MEAN_OF_VALUES
 mean_of_randomlist=sum(random_list)/len(random_list)
 print("Mean of values of list : " , mean_of_randomlist)
-
+#Output :
+#Mean of values of list :  50.45

From cf738432320545350f7cf706b1988b94fb1684b2 Mon Sep 17 00:00:00 2001
From: Javeria Tabassum <50498182+javeriatabassum145@users.noreply.github.com>
Date: Wed, 13 Oct 2021 11:54:02 +0500
Subject: [PATCH 026/232] Update Marksheet.py

---
 Python/Marksheet.py | 47 ++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 46 insertions(+), 1 deletion(-)

diff --git a/Python/Marksheet.py b/Python/Marksheet.py
index 47a11b8..b94de4c 100644
--- a/Python/Marksheet.py
+++ b/Python/Marksheet.py
@@ -1,14 +1,34 @@
+#Generating Marksheet of a Student
+
+#Taking input of name and roll number
 Name=input("Enter your name:")
+#Enter your name:Javeria
 Roll_no=input("Enter your roll no")
+#Enter your roll no01
+
+#Total marks 
 Total_marks=425
+
+#Taking input of all subjects one by one
 English=int(input("Enter you English marks out of 75:"))
+#Enter you English marks out of 75:60
 Pak_Studies=int(input("Enter you Pakistan Studies marks out of 75:"))
+#Enter you Pakistan Studies marks out of 75:70
 Computer=int(input("Enter you Computer marks out of 100:"))
+#Enter you Computer marks out of 100:100
 Chemistry=int(input("Enter you Chemistry marks out of 100:"))
+#Enter you Chemistry marks out of 100:90
 Sindhi=int(input("Enter you Sindhi marks out of 75:"))
+#Enter you Sindhi marks out of 75:65
+
+#Total obtained marks is calculated by adding all subject marks
 Total_obtained_marks=(English+Pak_Studies+Computer+Chemistry+Sindhi)
+
+#Calculating percentage by using percentage formula
 Percentage=((Total_obtained_marks/Total_marks)*100)
+
 print("*********************************************************** \n")
+
 print("\t \t  \" MARKSHEET SSC PART 1 \"  \n")
 print("Name        :",Name)
 print("Roll number : ",Roll_no)
@@ -21,6 +41,25 @@
 print("------------------------------------")
 print("                  ",Total_marks," ",Total_obtained_marks)
 print("\n")
+
+"""
+*********************************************************** 
+
+	 	  " MARKSHEET SSC PART 1 "  
+
+Name        : Javeria
+Roll number :  01
+Subjects          Total Obtained 
+English Marks       75   60
+Pak Studies Marks   75   70
+Computer Marks      100  100
+Chemistry Marks     100  90
+Sindhi Marks        75   65
+------------------------------------
+                   425   385
+"""
+
+#Printing the Percentage and calculating the grades
 if Percentage <= 100 and Percentage >= 80 :
     print("\t\" Your percentage is",Percentage,"and you got A+ Grade \"")
 elif Percentage < 80 and Percentage >= 70 :
@@ -33,5 +72,11 @@
     print("\t\" Your percentage is",Percentage,"and you got D Grade \"")
 else:
     print("\t\" You are fail \"")
+    
+print("\n ************************************************************")
+
+"""
+	" Your percentage is 90.58823529411765 and you got A+ Grade "
 
-print("\n ************************************************************")
\ No newline at end of file
+ ************************************************************
+ """

From 8f4d6cdadccfedfe6de270e915cbe6f87dbd8cd2 Mon Sep 17 00:00:00 2001
From: Javeria Tabassum <50498182+javeriatabassum145@users.noreply.github.com>
Date: Wed, 13 Oct 2021 17:48:32 +0500
Subject: [PATCH 027/232] Update Marksheet.py

---
 Python/Marksheet.py | 47 ++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 46 insertions(+), 1 deletion(-)

diff --git a/Python/Marksheet.py b/Python/Marksheet.py
index 47a11b8..b94de4c 100644
--- a/Python/Marksheet.py
+++ b/Python/Marksheet.py
@@ -1,14 +1,34 @@
+#Generating Marksheet of a Student
+
+#Taking input of name and roll number
 Name=input("Enter your name:")
+#Enter your name:Javeria
 Roll_no=input("Enter your roll no")
+#Enter your roll no01
+
+#Total marks 
 Total_marks=425
+
+#Taking input of all subjects one by one
 English=int(input("Enter you English marks out of 75:"))
+#Enter you English marks out of 75:60
 Pak_Studies=int(input("Enter you Pakistan Studies marks out of 75:"))
+#Enter you Pakistan Studies marks out of 75:70
 Computer=int(input("Enter you Computer marks out of 100:"))
+#Enter you Computer marks out of 100:100
 Chemistry=int(input("Enter you Chemistry marks out of 100:"))
+#Enter you Chemistry marks out of 100:90
 Sindhi=int(input("Enter you Sindhi marks out of 75:"))
+#Enter you Sindhi marks out of 75:65
+
+#Total obtained marks is calculated by adding all subject marks
 Total_obtained_marks=(English+Pak_Studies+Computer+Chemistry+Sindhi)
+
+#Calculating percentage by using percentage formula
 Percentage=((Total_obtained_marks/Total_marks)*100)
+
 print("*********************************************************** \n")
+
 print("\t \t  \" MARKSHEET SSC PART 1 \"  \n")
 print("Name        :",Name)
 print("Roll number : ",Roll_no)
@@ -21,6 +41,25 @@
 print("------------------------------------")
 print("                  ",Total_marks," ",Total_obtained_marks)
 print("\n")
+
+"""
+*********************************************************** 
+
+	 	  " MARKSHEET SSC PART 1 "  
+
+Name        : Javeria
+Roll number :  01
+Subjects          Total Obtained 
+English Marks       75   60
+Pak Studies Marks   75   70
+Computer Marks      100  100
+Chemistry Marks     100  90
+Sindhi Marks        75   65
+------------------------------------
+                   425   385
+"""
+
+#Printing the Percentage and calculating the grades
 if Percentage <= 100 and Percentage >= 80 :
     print("\t\" Your percentage is",Percentage,"and you got A+ Grade \"")
 elif Percentage < 80 and Percentage >= 70 :
@@ -33,5 +72,11 @@
     print("\t\" Your percentage is",Percentage,"and you got D Grade \"")
 else:
     print("\t\" You are fail \"")
+    
+print("\n ************************************************************")
+
+"""
+	" Your percentage is 90.58823529411765 and you got A+ Grade "
 
-print("\n ************************************************************")
\ No newline at end of file
+ ************************************************************
+ """

From b54ba82ddc15cd51516e52cd7ec6c9676e945cc1 Mon Sep 17 00:00:00 2001
From: Muskan12350 <66747331+Muskan12350@users.noreply.github.com>
Date: Wed, 13 Oct 2021 22:49:42 +0500
Subject: [PATCH 028/232] Added for loop example

Printed numbers from 1 to 10 and even numbers from 0 to 10
---
 Dart/forloop.dart | 15 +++++++++++++++
 1 file changed, 15 insertions(+)
 create mode 100644 Dart/forloop.dart

diff --git a/Dart/forloop.dart b/Dart/forloop.dart
new file mode 100644
index 0000000..4a46a1d
--- /dev/null
+++ b/Dart/forloop.dart
@@ -0,0 +1,15 @@
+void main () {
+  // Print numbers from 1 to 10
+  for(var i=1; i<=10; i++){
+    print(i);
+  }
+  
+  print("");
+  
+  // Print even numbers from 0 to 10
+  for(var j=0; j<=10; j++){
+    if(j%2==0){
+      print(j);
+    }
+  }
+}

From 76752a52c58b170372548b44f0edadc8d1c44bee Mon Sep 17 00:00:00 2001
From: Shoaib Khalid <shoaibkakil@gmail.com>
Date: Fri, 15 Oct 2021 16:23:09 +0500
Subject: [PATCH 029/232] Update Weirdalgo.cppp

added problem question with example and input/output.
---
 C++/Weirdalgo.cppp | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)

diff --git a/C++/Weirdalgo.cppp b/C++/Weirdalgo.cppp
index 15ce5fa..dd49fda 100644
--- a/C++/Weirdalgo.cppp
+++ b/C++/Weirdalgo.cppp
@@ -1,3 +1,7 @@
+
+//Consider an algorithm that takes as input a positive integer n. If n is even, the algorithm divides it by two, and if n is odd, the algorithm multiplies it by three and adds one. The algorithm repeats this, until n is one. For example, the sequence for n=3 is as follows:
+//3→10→5→16→8→4→2→1
+
 #include <iostream>
 using namespace std;
 
@@ -16,3 +20,24 @@ int main()
 	}
 	return 0;
 }
+
+
+//Input
+
+//The only input line contains an integer n.
+
+//utput
+
+P//rint a line that contains all values of n during the algorithm.
+
+//Constraints
+//1≤n≤106
+
+
+//Example
+
+//Input:
+//3
+
+//Output:
+//3 10 5 16 8 4 2 1

From 7380fd9cb60769b69bb1b0b1afe34d882f1f54b0 Mon Sep 17 00:00:00 2001
From: Muskan12350 <66747331+Muskan12350@users.noreply.github.com>
Date: Sun, 17 Oct 2021 15:33:32 +0500
Subject: [PATCH 030/232] Switch Case Code

Added an example on switch case
---
 switchcase.dart | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)
 create mode 100644 switchcase.dart

diff --git a/switchcase.dart b/switchcase.dart
new file mode 100644
index 0000000..08943f1
--- /dev/null
+++ b/switchcase.dart
@@ -0,0 +1,19 @@
+void main () {
+  var number = 100;
+  switch(number){
+    case 8:
+      print('Number is 8');
+      break;
+      
+    case 1:
+      print('Number is 1');
+      break;
+      
+    case 5:
+      print('Number is 5');
+      break;
+    default:
+      print("None of the number is matched");
+  }
+  
+}

From 70da9c3e131f76823f27884fac1628d6c4ed5a04 Mon Sep 17 00:00:00 2001
From: Muskan12350 <66747331+Muskan12350@users.noreply.github.com>
Date: Sun, 17 Oct 2021 15:43:45 +0500
Subject: [PATCH 031/232] Conditional Expressions Code

Added an example on conditional expressions
---
 Dart/conditional expressions.dart | 6 ++++++
 1 file changed, 6 insertions(+)
 create mode 100644 Dart/conditional expressions.dart

diff --git a/Dart/conditional expressions.dart b/Dart/conditional expressions.dart
new file mode 100644
index 0000000..ed7eb55
--- /dev/null
+++ b/Dart/conditional expressions.dart	
@@ -0,0 +1,6 @@
+void main () {
+  
+  var num = 13;
+  num%12==0 ? print("$num is Even") : print("$num is Odd");
+  
+}

From 4b07bc20e21c21364cfcc22bffce3566f270f8f8 Mon Sep 17 00:00:00 2001
From: Muskan12350 <66747331+Muskan12350@users.noreply.github.com>
Date: Sun, 17 Oct 2021 15:51:25 +0500
Subject: [PATCH 032/232] Continue Statement Code

Added an example on continue statement
---
 continue statement.dart | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 continue statement.dart

diff --git a/continue statement.dart b/continue statement.dart
new file mode 100644
index 0000000..6378f07
--- /dev/null
+++ b/continue statement.dart	
@@ -0,0 +1,8 @@
+void main () {
+ for(var i =1; i<=10; i++){
+   if(i==5) {
+     continue;
+   }
+   print(i);
+ }
+}

From c0033848381e54cb23239cd0e1de8d72454e75c6 Mon Sep 17 00:00:00 2001
From: Muskan12350 <66747331+Muskan12350@users.noreply.github.com>
Date: Sun, 17 Oct 2021 15:56:32 +0500
Subject: [PATCH 033/232] Break Statement Code

Added an example on break statement
---
 Dart/break statement.dart | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 Dart/break statement.dart

diff --git a/Dart/break statement.dart b/Dart/break statement.dart
new file mode 100644
index 0000000..41b7a15
--- /dev/null
+++ b/Dart/break statement.dart	
@@ -0,0 +1,8 @@
+void main () {
+ for(var i =1; i<=10; i++){
+   print(i);
+   if(i==7){
+     break;
+   }
+ }
+}

From 556e4b0dd603cc60ccf92ed848afdad3a27b38a0 Mon Sep 17 00:00:00 2001
From: hibarajput <50821244+hibarajput@users.noreply.github.com>
Date: Sun, 17 Oct 2021 04:10:28 -0700
Subject: [PATCH 034/232] Create Data Science_Libraries

---
 Data Science_Libraries | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 Data Science_Libraries

diff --git a/Data Science_Libraries b/Data Science_Libraries
new file mode 100644
index 0000000..3e26aae
--- /dev/null
+++ b/Data Science_Libraries	
@@ -0,0 +1,11 @@
+NUMPY: Numpy stands for numerical python. It is python's extension module and math library.Numpy id for all mathematical operations, arrays, matrices, and lists. 
+You can install numpy as:                   pip install numpy
+
+Pandas: Pandas is a library created to help developers work with "labeled" and "relational" data intuitively. It's based on two main data structures: "Series" and "Data Frames."
+You can install Pandas as:                 pip install manager and then pip install pandas
+
+Matplotlib: An alternative of MATLAB and extensiom of numpy that is used for plotting and data visualization. 
+You can install Matplotlib as:             install matplotlib
+
+Seaborn: Seaborn is an open-source Python library built on top of matplotlib. It is used for data visualization and exploratory data analysis.
+You can install it as:                      pip install seaborn

From 60c20cdc0565d98e308d25f8893f3222b43cf13e Mon Sep 17 00:00:00 2001
From: hibarajput <50821244+hibarajput@users.noreply.github.com>
Date: Sun, 17 Oct 2021 04:20:58 -0700
Subject: [PATCH 035/232] Create Delete operator Program

---
 Data Structures/Delete operator Program | 30 +++++++++++++++++++++++++
 1 file changed, 30 insertions(+)
 create mode 100644 Data Structures/Delete operator Program

diff --git a/Data Structures/Delete operator Program b/Data Structures/Delete operator Program
new file mode 100644
index 0000000..7551c1a
--- /dev/null
+++ b/Data Structures/Delete operator Program	
@@ -0,0 +1,30 @@
+Memory allocated dynamically is only needed during specific periods of time within a program; once it is no longer needed, it can be freed so that the memory becomes available again for other requests of dynamic memory. 
+This is the purpose of operator delete. The following is the sample program:
+
+include <iostream>
+#include <new>
+using namespace std;
+
+int main ()
+{
+  int i,n;
+  int * p;
+  cout << "How many numbers would you like to type? ";
+  cin >> i;
+  p= new int[i];
+  if (p == nullptr)
+    cout << "Error: memory could not be allocated";
+  else
+  {
+    for (n=0; n<i; n++)
+    {
+      cout << "Enter number: ";
+      cin >> p[n];
+    }
+    cout << "You have entered: ";
+    for (n=0; n<i; n++)
+      cout << p[n] << ", ";
+    delete[] p;
+  }
+  return 0;
+}

From 54a6aa70da0f18306684a8b1920d1d1fdffb3816 Mon Sep 17 00:00:00 2001
From: hibarajput <50821244+hibarajput@users.noreply.github.com>
Date: Sun, 17 Oct 2021 04:26:25 -0700
Subject: [PATCH 036/232] Create Recursion

---
 Data Structures/Recursion | 9 +++++++++
 1 file changed, 9 insertions(+)
 create mode 100644 Data Structures/Recursion

diff --git a/Data Structures/Recursion b/Data Structures/Recursion
new file mode 100644
index 0000000..cffe370
--- /dev/null
+++ b/Data Structures/Recursion	
@@ -0,0 +1,9 @@
+A Recursive task is a task that is defined in the terms of itself. Recursive function calls itself. 
+With each invocation, the problem is reduced to a smaller task (reducing case) until the task arrives at some terminal case. A recursive function has two parts:
+1.	Terminal/base case. a stopping conditions.
+2.	Reducing case/recursive step an expression of the computation or definition in terms of itself.
+Syntax:
+if (terminal_condition)
+     terminal_case
+else
+     reducing_case

From ac5c46c0996ff153ea02e3398533b0ed7a77525f Mon Sep 17 00:00:00 2001
From: hibarajput <50821244+hibarajput@users.noreply.github.com>
Date: Sun, 17 Oct 2021 04:30:58 -0700
Subject: [PATCH 037/232] Create max heap algorithms

---
 Data Structures/max heap algorithms | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)
 create mode 100644 Data Structures/max heap algorithms

diff --git a/Data Structures/max heap algorithms b/Data Structures/max heap algorithms
new file mode 100644
index 0000000..298a04c
--- /dev/null
+++ b/Data Structures/max heap algorithms	
@@ -0,0 +1,16 @@
+Max heap constuction algorithm
+It is about how to derive an algorithm for max heap by inserting one element at a time. 
+At any point of time, heap must maintain its property. While insertion, we assume that we are inserting a node in an already heapified tree.
+Step 1 − Create a new node at the end of heap.
+Step 2 − Assign new value to the node.
+Step 3 − Compare the value of this child node with its parent.
+Step 4 − If value of parent is less than child, then swap them.
+Step 5 − Repeat step 3 & 4 until Heap property holds.
+
+
+Max heap delete algorithm
+Step 1 − Remove root node.
+Step 2 − Move the last element of last level to root.
+Step 3 − Compare the value of this child node with its parent.
+Step 4 − If value of parent is less than child, then swap them.
+Step 5 − Repeat step 3 & 4 until Heap property holds.

From 55838aaedee18bdee406f0f0a9de1606913cfd55 Mon Sep 17 00:00:00 2001
From: Shoaib Khalid <shoaibkakil@gmail.com>
Date: Sun, 17 Oct 2021 20:20:49 +0500
Subject: [PATCH 038/232] Create NumberSpiral.cpp

---
 C++/NumberSpiral.cpp | 77 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 77 insertions(+)
 create mode 100644 C++/NumberSpiral.cpp

diff --git a/C++/NumberSpiral.cpp b/C++/NumberSpiral.cpp
new file mode 100644
index 0000000..f937bca
--- /dev/null
+++ b/C++/NumberSpiral.cpp
@@ -0,0 +1,77 @@
+//Problem link: https://cses.fi/problemset/task/1071/
+//Example
+
+//Input:
+//3
+//2 3
+//1 1
+//4 2
+
+//Output:
+//8
+//1
+//15
+
+#include <bits/stdc++.h>
+ 
+using namespace std;
+ 
+int main()
+{
+ 
+    int T;
+    cin >> T;
+ 
+    long long i;
+    long long j;
+    while (T--)
+    {
+        cin >> i >> j;
+ 
+        long long x = 0;
+        if (i == j)
+        {
+            x = (i * i) - (i - 1);
+        }
+        else if (i == 1 && j % 2 == 0)
+        { //even
+            x = ((j - 1) * (j - 1)) + 1;
+        }
+        else if (i == 1 && j % 2 != 0)
+        { //odd j=3// matrix[0][2]
+            x = j * j;
+        }
+        //firs column
+        else if (j == 1 && i % 2 == 0)
+        { //even
+            x = i * i;
+        }
+        else if (j == 1 && i % 2 != 0)
+        { //odd
+            x = (i - 1) * (i - 1) + 1;
+            //rest of the j matrix
+        }
+        else if (j > i && j % 2 != 0)
+        { // j=3 and i=2; matrix[1][2]
+            //   matrix[i - 1][j - 1] = matrix[i - 2][j - 1] - 1;
+            x = (j * j) - (i - 1);
+        }
+        else if (j > i && j % 2 == 0)
+        { // j=3 and i=2; matrix[1][2])
+            // matrix[i - 1][j - 1] = matrix[i - 2][j - 1] + 1;
+            x = (j - 1) * (j - 1) + (i);
+        }
+        //rest of the i matrix
+        else if (i > j && i % 2 != 0)
+        { // i=3 and j=2; matrix[2][1]
+            // matrix[i - 1][j - 1] = matrix[i - 1][j - 2] + 1;
+            x = (i - 1) * (i - 1) + j;
+        }
+        else if (i > j && i % 2 == 0)
+        { // i=3 and j=2; matrix[2][1]
+            x = (i * i) - (j - 1);
+        }
+ 
+        cout << x << endl;
+    }
+}

From b614dae4dc2ccdcd1200f99b6ee1f8ebf124557f Mon Sep 17 00:00:00 2001
From: Shoaib Khalid <shoaibkakil@gmail.com>
Date: Sun, 17 Oct 2021 20:25:40 +0500
Subject: [PATCH 039/232] Create TowSets.cpp

---
 C++/TowSets.cpp | 87 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 87 insertions(+)
 create mode 100644 C++/TowSets.cpp

diff --git a/C++/TowSets.cpp b/C++/TowSets.cpp
new file mode 100644
index 0000000..0978586
--- /dev/null
+++ b/C++/TowSets.cpp
@@ -0,0 +1,87 @@
+//Problem link: https://cses.fi/problemset/task/1092/
+
+
+//Example 1
+
+//Input:
+//7
+//Output:
+//YES
+//4
+//1 2 4 7
+//3
+//3 5 6
+
+
+//Example 2
+
+//Input:
+//6
+//Output:
+//NO
+
+#include <bits/stdc++.h>
+ 
+using namespace std;
+ 
+#define ll long long int
+ 
+int main()
+{
+ 
+    ll n;
+    cin >> n;
+    vector<ll> S1;
+    vector<ll> S2;
+ 
+    ll total = 0;
+    for (int i = 1; i <= n; i++)
+    {
+        total += i;
+    }
+ 
+    ll half = total / 2;
+    ll gSum = 0;
+ 
+    if (total%2==0)
+    {
+        cout << "YES" << endl;
+ 
+        for (ll i = n; i >= 1; i--)
+        {
+ 
+            if (gSum + i <= half)
+            {
+                gSum += i;
+                S1.push_back(i);
+            }
+            else
+            {
+                S2.push_back(i);
+            }
+        }
+ 
+        ll S1_size = S1.size();
+        ll S2_size = S2.size();
+ 
+        cout << S2_size << endl;
+        for (int i = S2_size - 1; i >= 0; i--)
+        {
+            cout << S2[i] << " ";
+        }
+        cout << endl;
+ 
+        cout << S1_size << endl;
+        for (int i = S1_size - 1; i >= 0; i--)
+        {
+            cout << S1[i] << " ";
+        }
+        cout << endl;
+    }
+    else
+    {
+        cout << "NO" << endl;
+    }
+ 
+    return 0;
+}

From 0926b2016983518ce83df4078f13676628515778 Mon Sep 17 00:00:00 2001
From: Punit Sakre <punitsakre786@gmail.com>
Date: Mon, 18 Oct 2021 11:56:39 +0530
Subject: [PATCH 040/232] Added code files for Javascript

---
 Javascript/ArrayReversal.js                    |  2 ++
 Javascript/CharacterSearchInString.js          |  7 +++++++
 ...layElementsContainingParticularCharacter.js |  6 ++++++
 Javascript/MinutesToSeconds.js                 |  3 +++
 Javascript/SearchingElementInArrayOfStrings.js | 18 ++++++++++++++++++
 5 files changed, 36 insertions(+)
 create mode 100644 Javascript/ArrayReversal.js
 create mode 100644 Javascript/CharacterSearchInString.js
 create mode 100644 Javascript/DisplayElementsContainingParticularCharacter.js
 create mode 100644 Javascript/MinutesToSeconds.js
 create mode 100644 Javascript/SearchingElementInArrayOfStrings.js

diff --git a/Javascript/ArrayReversal.js b/Javascript/ArrayReversal.js
new file mode 100644
index 0000000..8963640
--- /dev/null
+++ b/Javascript/ArrayReversal.js
@@ -0,0 +1,2 @@
+let revarr = ['Bus', 'Star', 'Hybrid', 'Ring', 'Tree'];
+console.log(revarr.reverse());
diff --git a/Javascript/CharacterSearchInString.js b/Javascript/CharacterSearchInString.js
new file mode 100644
index 0000000..632e97d
--- /dev/null
+++ b/Javascript/CharacterSearchInString.js
@@ -0,0 +1,7 @@
+let str = 'Program to search for a particular character in a string';
+
+// To print the index of the charater
+console.log(str.indexOf('f'));
+
+// To print that the character is in the string or not
+console.log(str.includes('s'));
diff --git a/Javascript/DisplayElementsContainingParticularCharacter.js b/Javascript/DisplayElementsContainingParticularCharacter.js
new file mode 100644
index 0000000..89c434d
--- /dev/null
+++ b/Javascript/DisplayElementsContainingParticularCharacter.js
@@ -0,0 +1,6 @@
+let arr = ['Speaker', 'Monitor', 'Keyboard', 'Mouse', 'Printer'];
+for (var i = 0; i < arr.length; i++) {
+    if (arr[i].includes('a')) {
+        console.log(arr[i]);
+    }
+}
diff --git a/Javascript/MinutesToSeconds.js b/Javascript/MinutesToSeconds.js
new file mode 100644
index 0000000..b1b8c5e
--- /dev/null
+++ b/Javascript/MinutesToSeconds.js
@@ -0,0 +1,3 @@
+let min = prompt('Enter minutes :');
+const sec = 60;
+console.log('Minutes to Seconds : ' + min * sec + ' seconds');
diff --git a/Javascript/SearchingElementInArrayOfStrings.js b/Javascript/SearchingElementInArrayOfStrings.js
new file mode 100644
index 0000000..43053e4
--- /dev/null
+++ b/Javascript/SearchingElementInArrayOfStrings.js
@@ -0,0 +1,18 @@
+let str = ['Java', 'Python', 'Javascript', 'Django', 'Angular', 'Nodejs'];
+let val = prompt('Enter Element name :');
+
+// Using array function
+console.log(str.includes(val));
+
+// Using if statement
+if (str.includes(val)) {
+    console.log(val);
+} else {
+    console.log('Element not found!!!');
+}
+
+// for (let j=0; j<str.length; j++) {
+//     if (str[j].match(val)) {
+//         console.log(val)
+//     }
+// }

From 4769161078cbdf0a71e6e261fa0d39f252404b7a Mon Sep 17 00:00:00 2001
From: Punit Sakre <punitsakre786@gmail.com>
Date: Mon, 18 Oct 2021 19:36:29 +0530
Subject: [PATCH 041/232] Made appropriate changes as asked

---
 Javascript/ArrayReversal.js                          |  9 +++++++++
 Javascript/CharacterSearchInString.js                |  6 ++++++
 .../DisplayElementsContainingParticularCharacter.js  |  4 ++++
 Javascript/MinutesToSeconds.js                       |  4 +++-
 Javascript/SearchingElementInArrayOfStrings.js       | 12 +++++++-----
 5 files changed, 29 insertions(+), 6 deletions(-)

diff --git a/Javascript/ArrayReversal.js b/Javascript/ArrayReversal.js
index 8963640..db2e16f 100644
--- a/Javascript/ArrayReversal.js
+++ b/Javascript/ArrayReversal.js
@@ -1,2 +1,11 @@
 let revarr = ['Bus', 'Star', 'Hybrid', 'Ring', 'Tree'];
 console.log(revarr.reverse());
+
+// Output
+// (5) ['Tree', 'Ring', 'Hybrid', 'Star', 'Bus']
+// 0: "Tree"
+// 1: "Ring"
+// 2: "Hybrid"
+// 3: "Star"
+// 4: "Bus"
+// length: 5
diff --git a/Javascript/CharacterSearchInString.js b/Javascript/CharacterSearchInString.js
index 632e97d..42ee3ce 100644
--- a/Javascript/CharacterSearchInString.js
+++ b/Javascript/CharacterSearchInString.js
@@ -3,5 +3,11 @@ let str = 'Program to search for a particular character in a string';
 // To print the index of the charater
 console.log(str.indexOf('f'));
 
+// Output
+// 18
+
 // To print that the character is in the string or not
 console.log(str.includes('s'));
+
+// Output
+// true
diff --git a/Javascript/DisplayElementsContainingParticularCharacter.js b/Javascript/DisplayElementsContainingParticularCharacter.js
index 89c434d..396efa0 100644
--- a/Javascript/DisplayElementsContainingParticularCharacter.js
+++ b/Javascript/DisplayElementsContainingParticularCharacter.js
@@ -4,3 +4,7 @@ for (var i = 0; i < arr.length; i++) {
         console.log(arr[i]);
     }
 }
+
+// Output
+// Speaker
+// Keyboard
diff --git a/Javascript/MinutesToSeconds.js b/Javascript/MinutesToSeconds.js
index b1b8c5e..7f021ac 100644
--- a/Javascript/MinutesToSeconds.js
+++ b/Javascript/MinutesToSeconds.js
@@ -1,3 +1,5 @@
-let min = prompt('Enter minutes :');
+let min = prompt('Enter minutes :'); // Input : 5
 const sec = 60;
 console.log('Minutes to Seconds : ' + min * sec + ' seconds');
+
+// Output : 300 seconds
diff --git a/Javascript/SearchingElementInArrayOfStrings.js b/Javascript/SearchingElementInArrayOfStrings.js
index 43053e4..ee0247f 100644
--- a/Javascript/SearchingElementInArrayOfStrings.js
+++ b/Javascript/SearchingElementInArrayOfStrings.js
@@ -11,8 +11,10 @@ if (str.includes(val)) {
     console.log('Element not found!!!');
 }
 
-// for (let j=0; j<str.length; j++) {
-//     if (str[j].match(val)) {
-//         console.log(val)
-//     }
-// }
+// Input : Jav
+// Output : false
+//          Element not found!!!
+
+// Input : Java
+// Output : true
+//          Java

From 324f44c5d156b3f3ec71e0f742827e0117930818 Mon Sep 17 00:00:00 2001
From: Farhan Khan <92777802+AlyfarhanKhan@users.noreply.github.com>
Date: Tue, 19 Oct 2021 23:39:06 +0500
Subject: [PATCH 042/232] Create Play_Tennis_Decision_Tree.py

---
 Python/Play_Tennis_Decision_Tree.py | 36 +++++++++++++++++++++++++++++
 1 file changed, 36 insertions(+)
 create mode 100644 Python/Play_Tennis_Decision_Tree.py

diff --git a/Python/Play_Tennis_Decision_Tree.py b/Python/Play_Tennis_Decision_Tree.py
new file mode 100644
index 0000000..61e3d48
--- /dev/null
+++ b/Python/Play_Tennis_Decision_Tree.py
@@ -0,0 +1,36 @@
+# Load libraries
+import numpy as np
+import pandas as pd
+from sklearn import metrics  # Import scikit-learn metrics module for accuracy calculation
+from sklearn import preprocessing
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier  # import the classifier
+
+df = pd.read_csv("Play Tennis.csv")
+
+
+string_to_int = preprocessing.LabelEncoder()  # encode your data
+df = df.apply(string_to_int.fit_transform)  # fit and transform it
+# To divide our data into attribute set and Label:
+feature_cols = ['Outlook', 'Temprature', 'Humidity', 'Wind']
+X = df[feature_cols]  # contains the attribute
+y = df.Play_Tennis  # contains the label
+
+# To divide our data into training and test sets:
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+# perform training
+
+classifier = DecisionTreeClassifier(criterion="entropy")  # create a classifier object
+classifier.fit(X_train, y_train)  # fit the classifier with X and Y data
+# Predict the response for test dataset
+y_pred = classifier.predict(X_test)
+print(y_pred)
+pred = classifier.predict([[0, 1, 0, 1]])
+print(pred)
+
+# Model Accuracy, how often is the classifier correct?
+
+
+print("Accuracy:", metrics.accuracy_score(y_test, y_pred))

From c9e8f62e070c99b2c8882ef5dfb2281507ef02ef Mon Sep 17 00:00:00 2001
From: Madni Mahenti <77567956+madniraza23@users.noreply.github.com>
Date: Wed, 20 Oct 2021 13:47:04 +0500
Subject: [PATCH 043/232] Create selectionSort.cs

---
 C#/Sorting/selectionSort.cs | 46 +++++++++++++++++++++++++++++++++++++
 1 file changed, 46 insertions(+)
 create mode 100644 C#/Sorting/selectionSort.cs

diff --git a/C#/Sorting/selectionSort.cs b/C#/Sorting/selectionSort.cs
new file mode 100644
index 0000000..b817a0c
--- /dev/null
+++ b/C#/Sorting/selectionSort.cs
@@ -0,0 +1,46 @@
+// C# program for implementation
+// of Selection Sort
+using System;
+
+class GFG
+{
+	static void sort(int []arr)
+	{
+		int n = arr.Length;
+
+		// One by one move boundary of unsorted subarray
+		for (int i = 0; i < n - 1; i++)
+		{
+			// Find the minimum element in unsorted array
+			int min_idx = i;
+			for (int j = i + 1; j < n; j++)
+				if (arr[j] < arr[min_idx])
+					min_idx = j;
+
+			// Swap the found minimum element with the first
+			// element
+			int temp = arr[min_idx];
+			arr[min_idx] = arr[i];
+			arr[i] = temp;
+		}
+	}
+
+	// Prints the array
+	static void printArray(int []arr)
+	{
+		int n = arr.Length;
+		for (int i=0; i<n; ++i)
+			Console.Write(arr[i]+" ");
+		Console.WriteLine();
+	}
+
+	// Driver code
+	public static void Main()
+	{
+		int []arr = {64,25,12,22,11};
+		sort(arr);
+		Console.WriteLine("Sorted array");
+		printArray(arr);
+	}
+
+}

From 8edf669929a9e7865233160e068707ce645b4376 Mon Sep 17 00:00:00 2001
From: Madni Mahenti <77567956+madniraza23@users.noreply.github.com>
Date: Wed, 20 Oct 2021 13:49:43 +0500
Subject: [PATCH 044/232] Create BubbleSort

---
 C#/Sorting/BubbleSort | 39 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 39 insertions(+)
 create mode 100644 C#/Sorting/BubbleSort

diff --git a/C#/Sorting/BubbleSort b/C#/Sorting/BubbleSort
new file mode 100644
index 0000000..a2ebd12
--- /dev/null
+++ b/C#/Sorting/BubbleSort
@@ -0,0 +1,39 @@
+// C# program for implementation
+// of Bubble Sort
+using System;
+ 
+class GFG
+{
+    static void bubbleSort(int []arr)
+    {
+        int n = arr.Length;
+        for (int i = 0; i < n - 1; i++)
+            for (int j = 0; j < n - i - 1; j++)
+                if (arr[j] > arr[j + 1])
+                {
+                    // swap temp and arr[i]
+                    int temp = arr[j];
+                    arr[j] = arr[j + 1];
+                    arr[j + 1] = temp;
+                }
+    }
+ 
+    /* Prints the array */
+    static void printArray(int []arr)
+    {
+        int n = arr.Length;
+        for (int i = 0; i < n; ++i)
+            Console.Write(arr[i] + " ");
+        Console.WriteLine();
+    }
+ 
+    // Driver method
+    public static void Main()
+    {
+        int []arr = {64, 34, 25, 12, 22, 11, 90};
+        bubbleSort(arr);
+        Console.WriteLine("Sorted array");
+        printArray(arr);
+    }
+ 
+}

From 3c8253e83d6444a2e2dbbac2c525194853c2f6c1 Mon Sep 17 00:00:00 2001
From: Madni Mahenti <77567956+madniraza23@users.noreply.github.com>
Date: Wed, 20 Oct 2021 13:51:04 +0500
Subject: [PATCH 045/232] Create BitonicSort

---
 C#/Sorting/BitonicSort | 86 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 86 insertions(+)
 create mode 100644 C#/Sorting/BitonicSort

diff --git a/C#/Sorting/BitonicSort b/C#/Sorting/BitonicSort
new file mode 100644
index 0000000..de0e45f
--- /dev/null
+++ b/C#/Sorting/BitonicSort
@@ -0,0 +1,86 @@
+/* C# Program for Bitonic Sort. Note that this program
+works only when size of input is a power of 2. */
+using System;
+
+/*The parameter dir indicates the sorting direction, ASCENDING
+or DESCENDING; if (a[i] > a[j]) agrees with the direction,
+then a[i] and a[j] are interchanged.*/
+class GFG
+{
+	/* To swap values */
+	static void Swap<T>(ref T lhs, ref T rhs)
+	{
+		T temp;
+		temp = lhs;
+		lhs = rhs;
+		rhs = temp;
+	}
+	public static void compAndSwap(int[] a, int i, int j, int dir)
+	{
+		int k;
+		if((a[i]>a[j]))
+			k=1;
+		else
+			k=0;
+		if (dir==k)
+			Swap(ref a[i],ref a[j]);
+	}
+	
+	/*It recursively sorts a bitonic sequence in ascending order,
+	if dir = 1, and in descending order otherwise (means dir=0).
+	The sequence to be sorted starts at index position low,
+	the parameter cnt is the number of elements to be sorted.*/
+	public static void bitonicMerge(int[] a, int low, int cnt, int dir)
+	{
+		if (cnt>1)
+		{
+			int k = cnt/2;
+			for (int i=low; i<low+k; i++)
+				compAndSwap(a, i, i+k, dir);
+			bitonicMerge(a, low, k, dir);
+			bitonicMerge(a, low+k, k, dir);
+		}
+	}
+	
+	/* This function first produces a bitonic sequence by recursively
+		sorting its two halves in opposite sorting orders, and then
+		calls bitonicMerge to make them in the same order */
+	public static void bitonicSort(int[] a,int low, int cnt, int dir)
+	{
+		if (cnt>1)
+		{
+			int k = cnt/2;
+	
+			// sort in ascending order since dir here is 1
+			bitonicSort(a, low, k, 1);
+	
+			// sort in descending order since dir here is 0
+			bitonicSort(a, low+k, k, 0);
+	
+			// Will merge whole sequence in ascending order
+			// since dir=1.
+			bitonicMerge(a,low, cnt, dir);
+		}
+	}
+	
+	/* Caller of bitonicSort for sorting the entire array of
+	length N in ASCENDING order */
+	public static void sort(int[] a, int N, int up)
+	{
+		bitonicSort(a,0, N, up);
+	}
+	
+	// Driver code
+	static void Main()
+	{
+		int[] a= {3, 7, 4, 8, 6, 2, 1, 5};
+		int N = a.Length;
+	
+		int up = 1; // means sort in ascending order
+		sort(a, N, up);
+	
+		Console.Write("Sorted array: \n");
+		for (int i=0; i<N; i++)
+			Console.Write(a[i] + " ");
+		}
+}

From c204090970e1375951be14e96314a6be3e0f60d8 Mon Sep 17 00:00:00 2001
From: Madni Mahenti <77567956+madniraza23@users.noreply.github.com>
Date: Wed, 20 Oct 2021 13:52:10 +0500
Subject: [PATCH 046/232] Create PancakeSorting

---
 C#/Sorting/PancakeSorting | 97 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 97 insertions(+)
 create mode 100644 C#/Sorting/PancakeSorting

diff --git a/C#/Sorting/PancakeSorting b/C#/Sorting/PancakeSorting
new file mode 100644
index 0000000..2d4bf43
--- /dev/null
+++ b/C#/Sorting/PancakeSorting
@@ -0,0 +1,97 @@
+// C# program to sort array using
+// pancake sort
+using System;
+
+class GFG {
+
+	// Reverses arr[0..i]
+	static void flip(int []arr, int i)
+	{
+		int temp, start = 0;
+		while (start < i)
+		{
+			temp = arr[start];
+			arr[start] = arr[i];
+			arr[i] = temp;
+			start++;
+			i--;
+		}
+	}
+
+	// Returns index of the
+	// maximum element in
+	// arr[0..n-1]
+	static int findMax(int []arr, int n)
+	{
+		int mi, i;
+		for (mi = 0, i = 0; i < n; ++i)
+			if (arr[i] > arr[mi])
+				mi = i;
+				
+		return mi;
+	}
+
+	// The main function that
+	// sorts given array using
+	// flip operations
+	static int pancakeSort(int []arr, int n)
+	{
+		
+		// Start from the complete
+		// array and one by one
+		// reduce current size by one
+		for (int curr_size = n; curr_size > 1;
+								--curr_size)
+		{
+			
+			// Find index of the
+			// maximum element in
+			// arr[0..curr_size-1]
+			int mi = findMax(arr, curr_size);
+
+			// Move the maximum element
+			// to end of current array
+			// if it's not already at
+			// the end
+			if (mi != curr_size - 1)
+			{
+				// To move at the end,
+				// first move maximum
+				// number to beginning
+				flip(arr, mi);
+
+				// Now move the maximum
+				// number to end by
+				// reversing current array
+				flip(arr, curr_size - 1);
+			}
+		}
+		
+		return 0;
+	}
+
+	// Utility function to print
+	// array arr[]
+	static void printArray(int []arr,
+						int arr_size)
+	{
+		for (int i = 0; i < arr_size; i++)
+			Console.Write(arr[i] + " ");
+			
+		Console.Write("");
+	}
+
+	// Driver function to check for
+	// above functions
+	public static void Main ()
+	{
+		int []arr = {23, 10, 20, 11, 12, 6, 7};
+		int n = arr.Length;
+		
+		pancakeSort(arr, n);
+		
+		Console.Write("Sorted Array: ");
+		printArray(arr, n);
+	}
+}
+

From f28c70297860e64defeeca78efe2b7c46d92f24d Mon Sep 17 00:00:00 2001
From: Shoaib <shoaibkakil@gmail.com>
Date: Wed, 20 Oct 2021 18:38:03 +0500
Subject: [PATCH 047/232] Create Permutation.cpp

---
 C++/Permutation.cpp | 53 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 C++/Permutation.cpp

diff --git a/C++/Permutation.cpp b/C++/Permutation.cpp
new file mode 100644
index 0000000..f68be37
--- /dev/null
+++ b/C++/Permutation.cpp
@@ -0,0 +1,53 @@
+#include <bits/stdc++.h>
+using namespace std;
+
+
+
+int main()
+{
+ long long n;
+ cin >>n;
+ long long p[n];
+ int count=0;
+ int flag = 0;
+ long long i;
+ for(i=1; i<=n; i++)
+ {
+    if(i%2==0)
+    {
+        p[count] = i;
+        count++;
+    }
+ }
+ for( i=1; i<=n; i++)
+ {
+    if(i%2!=0)
+    {
+
+        p[count] =i;
+        count++;
+    }
+ }
+
+
+ for( i=0; i<n-1; i++ )
+ {
+     if(p[i] == p[i+1]-1 || p[i] == p[i+1]+1 )
+     {
+        flag = 1;
+    }
+ }
+
+
+
+ if(flag==0)
+ {
+    for(long long j=0; j<n; j++)
+    {
+        cout << p[j]<< " ";
+    }
+ }
+ else{
+    cout <<"NO SOLUTION";
+ }
+}

From 1ca533c276b17b2a0e7f03421d41c5f2b28d9019 Mon Sep 17 00:00:00 2001
From: Shoaib <shoaibkakil@gmail.com>
Date: Thu, 21 Oct 2021 10:38:50 +0500
Subject: [PATCH 048/232] Update Permutation.cpp

added comments for a better understanding of code.
---
 C++/Permutation.cpp | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/C++/Permutation.cpp b/C++/Permutation.cpp
index f68be37..1ddf023 100644
--- a/C++/Permutation.cpp
+++ b/C++/Permutation.cpp
@@ -6,11 +6,14 @@ using namespace std;
 int main()
 {
  long long n;
+ //take input; n is the number whom permutation has to find
  cin >>n;
  long long p[n];
  int count=0;
  int flag = 0;
  long long i;
+ 
+ // loop to add even numbers inside p[].
  for(i=1; i<=n; i++)
  {
     if(i%2==0)
@@ -19,6 +22,8 @@ int main()
         count++;
     }
  }
+ 
+ // loop to add odd numbers inside p[] 
  for( i=1; i<=n; i++)
  {
     if(i%2!=0)
@@ -30,6 +35,7 @@ int main()
  }
 
 
+ // Now by comparing elements inside p[], we find out if the permutation of n is possible or not.
  for( i=0; i<n-1; i++ )
  {
      if(p[i] == p[i+1]-1 || p[i] == p[i+1]+1 )
@@ -40,6 +46,7 @@ int main()
 
 
 
+ // from above comparision flag=0 means permutation is possible.
  if(flag==0)
  {
     for(long long j=0; j<n; j++)

From 621a591f81597762ca07ff404df45033887949f0 Mon Sep 17 00:00:00 2001
From: Mohammad Umair <55786752+mohammadumair1244@users.noreply.github.com>
Date: Fri, 22 Oct 2021 09:53:41 +0500
Subject: [PATCH 049/232] Create palindrome.cpp

---
 C++/palindrome.cpp | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)
 create mode 100644 C++/palindrome.cpp

diff --git a/C++/palindrome.cpp b/C++/palindrome.cpp
new file mode 100644
index 0000000..64841a3
--- /dev/null
+++ b/C++/palindrome.cpp
@@ -0,0 +1,28 @@
+#include <iostream>
+using namespace std;
+
+int main()
+{
+     int n, num, digit, rev = 0;
+
+     cout << "Enter a positive number: ";
+     cin >> num;
+
+     n = num;
+
+     do
+     {
+         digit = num % 10;
+         rev = (rev * 10) + digit;
+         num = num / 10;
+     } while (num != 0);
+
+     cout << " The reverse of the number is: " << rev << endl;
+
+     if (n == rev)
+         cout << " The number is a palindrome.";
+     else
+         cout << " The number is not a palindrome.";
+
+    return 0;
+}

From a4776afbf3772cfe909ecb1b0158733d7c13de37 Mon Sep 17 00:00:00 2001
From: Aneeq Khurram <59835270+aneeqkhurram007@users.noreply.github.com>
Date: Fri, 22 Oct 2021 09:55:51 +0500
Subject: [PATCH 050/232] Sorting Algorithm

---
 Javascript/sorting algorithm | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)
 create mode 100644 Javascript/sorting algorithm

diff --git a/Javascript/sorting algorithm b/Javascript/sorting algorithm
new file mode 100644
index 0000000..f8af55b
--- /dev/null
+++ b/Javascript/sorting algorithm	
@@ -0,0 +1,21 @@
+function bubbleSort(arr){
+
+    //Outer pass
+    for(let i = 0; i < arr.length; i++){
+
+        //Inner pass
+        for(let j = 0; j < arr.length - i - 1; j++){
+
+            //Value comparison using ascending order
+
+            if(arr[j + 1] < arr[j]){
+
+                //Swapping
+                [arr[j + 1],arr[j]] = [arr[j],arr[j + 1]]
+            }
+        }
+    };
+    return arr;
+};
+
+console.log(bubbleSort([5,3,8,4,6]));

From e0549d05d0a1632e67d9c98b9bb29bd806a7c99a Mon Sep 17 00:00:00 2001
From: Bilal Ahmad <50988601+billypentester@users.noreply.github.com>
Date: Fri, 22 Oct 2021 09:57:50 +0500
Subject: [PATCH 051/232] create LCM.js

---
 Javascript/LCM.js | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)
 create mode 100644 Javascript/LCM.js

diff --git a/Javascript/LCM.js b/Javascript/LCM.js
new file mode 100644
index 0000000..eda5544
--- /dev/null
+++ b/Javascript/LCM.js
@@ -0,0 +1,25 @@
+function lcm_more_than_two_numbers(input_array) {
+    if (toString.call(input_array) !== "[object Array]")  
+        return  false;  
+ var r1 = 0, r2 = 0;
+    var l = input_array.length;
+    for(i=0;i<l;i++) {
+        r1 = input_array[i] % input_array[i + 1];
+        if(r1 === 0) {
+            input_array[i + 1] = (input_array[i] * input_array[i+1]) / input_array[i + 1];
+        }
+        else {
+            r2 = input_array[i + 1] % r1;
+            if(r2 === 0) {
+                input_array[i + 1] = (input_array[i] * input_array[i + 1]) / r1;
+            }
+            else {
+                input_array[i+1] = (input_array[i] * input_array[i + 1]) / r2;
+            }
+        }
+    }
+    return input_array[l - 1];
+}
+
+console.log(lcm_more_than_two_numbers([100,90,80,7])); 
+console.log(lcm_more_than_two_numbers([5,10,15,25]));

From 4c3057d4380bf3b3268d47a8dc1f4f0cd547ec76 Mon Sep 17 00:00:00 2001
From: Mohammad Umair <55786752+mohammadumair1244@users.noreply.github.com>
Date: Fri, 22 Oct 2021 09:58:12 +0500
Subject: [PATCH 052/232] Create fibonacci.py

---
 Python/fibonacci.py | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)
 create mode 100644 Python/fibonacci.py

diff --git a/Python/fibonacci.py b/Python/fibonacci.py
new file mode 100644
index 0000000..ffbdece
--- /dev/null
+++ b/Python/fibonacci.py
@@ -0,0 +1,25 @@
+# Program to display the Fibonacci sequence up to n-th term
+
+nterms = int(input("How many terms? "))
+
+# first two terms
+n1, n2 = 0, 1
+count = 0
+
+# check if the number of terms is valid
+if nterms <= 0:
+   print("Please enter a positive integer")
+# if there is only one term, return n1
+elif nterms == 1:
+   print("Fibonacci sequence upto",nterms,":")
+   print(n1)
+# generate fibonacci sequence
+else:
+   print("Fibonacci sequence:")
+   while count < nterms:
+       print(n1)
+       nth = n1 + n2
+       # update values
+       n1 = n2
+       n2 = nth
+       count += 1

From 0f7f9b50a11763ee3d1b37e4cd94ac376c8dec0b Mon Sep 17 00:00:00 2001
From: Bilal Ahmad <50988601+billypentester@users.noreply.github.com>
Date: Fri, 22 Oct 2021 10:02:59 +0500
Subject: [PATCH 053/232] crerate nthroot.js

---
 Javascript/nthroot.js | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 Javascript/nthroot.js

diff --git a/Javascript/nthroot.js b/Javascript/nthroot.js
new file mode 100644
index 0000000..c753e87
--- /dev/null
+++ b/Javascript/nthroot.js
@@ -0,0 +1,13 @@
+function nthroot(x, n)
+   {
+    ng = n % 2;
+    if((ng == 1) || x<0)
+       x = -x;
+    var r = Math.pow(x, 1 / n);
+    n = Math.pow(r, n);
+  
+    if(Math.abs(x - n) < 1 && (x > 0 === n > 0))
+      return ng ? -r : r; 
+   }
+console.log(nthroot(64, 2));
+console.log(nthroot(64, -2));

From f964fa4dee034e815213058587f6e9f9d2676aae Mon Sep 17 00:00:00 2001
From: Bilal Ahmad <50988601+billypentester@users.noreply.github.com>
Date: Fri, 22 Oct 2021 10:04:17 +0500
Subject: [PATCH 054/232] created nthroot.js

---
 Javascript/nthroot.js | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 Javascript/nthroot.js

diff --git a/Javascript/nthroot.js b/Javascript/nthroot.js
new file mode 100644
index 0000000..c753e87
--- /dev/null
+++ b/Javascript/nthroot.js
@@ -0,0 +1,13 @@
+function nthroot(x, n)
+   {
+    ng = n % 2;
+    if((ng == 1) || x<0)
+       x = -x;
+    var r = Math.pow(x, 1 / n);
+    n = Math.pow(r, n);
+  
+    if(Math.abs(x - n) < 1 && (x > 0 === n > 0))
+      return ng ? -r : r; 
+   }
+console.log(nthroot(64, 2));
+console.log(nthroot(64, -2));

From 3a3aa2dd772ac13e4ab069c6af11841cae7040e0 Mon Sep 17 00:00:00 2001
From: Kainat-Mahar <72600530+Kainat-Mahar@users.noreply.github.com>
Date: Fri, 22 Oct 2021 13:27:31 +0500
Subject: [PATCH 055/232] Create Heart Disease Predictions

---
 Data Science/Heart Disease Predictions | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 Data Science/Heart Disease Predictions

diff --git a/Data Science/Heart Disease Predictions b/Data Science/Heart Disease Predictions
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/Data Science/Heart Disease Predictions	
@@ -0,0 +1 @@
+

From adf3becf6171bb464cefa90fef91d70d724d273a Mon Sep 17 00:00:00 2001
From: Kainat-Mahar <72600530+Kainat-Mahar@users.noreply.github.com>
Date: Fri, 22 Oct 2021 13:32:10 +0500
Subject: [PATCH 056/232] Heart Disease Prediction Using SVM

---
 Data Science/DSA_Project_on_SVM.ipynb | 1461 +++++++++++++++++++++++++
 Data Science/dataset.csv              |  304 +++++
 2 files changed, 1765 insertions(+)
 create mode 100644 Data Science/DSA_Project_on_SVM.ipynb
 create mode 100644 Data Science/dataset.csv

diff --git a/Data Science/DSA_Project_on_SVM.ipynb b/Data Science/DSA_Project_on_SVM.ipynb
new file mode 100644
index 0000000..70a208b
--- /dev/null
+++ b/Data Science/DSA_Project_on_SVM.ipynb	
@@ -0,0 +1,1461 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "DSA Project on SVM.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HbbGwEA47kDj"
+      },
+      "source": [
+        "Importing Libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "roYk-Q3s6yVd"
+      },
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib as plt\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7v8I08N58jcA"
+      },
+      "source": [
+        "Data Wrangling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ttq0PCrZ8i_d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 202
+        },
+        "outputId": "9302ee67-8287-4ddb-d345-c7df0832f6cd"
+      },
+      "source": [
+        "data = pd.read_csv('dataset.csv')\n",
+        "\n",
+        "data.head(5)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>cp</th>\n",
+              "      <th>trestbps</th>\n",
+              "      <th>chol</th>\n",
+              "      <th>fbs</th>\n",
+              "      <th>restecg</th>\n",
+              "      <th>thalach</th>\n",
+              "      <th>exang</th>\n",
+              "      <th>oldpeak</th>\n",
+              "      <th>slope</th>\n",
+              "      <th>ca</th>\n",
+              "      <th>thal</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>63</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>145</td>\n",
+              "      <td>233</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>150</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>37</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>130</td>\n",
+              "      <td>250</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>187</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>41</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>130</td>\n",
+              "      <td>204</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>172</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>56</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>120</td>\n",
+              "      <td>236</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>178</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>57</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>120</td>\n",
+              "      <td>354</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>163</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
+              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
+              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
+              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
+              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
+              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
+              "\n",
+              "[5 rows x 14 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8PJwi9rc8tp-",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 86
+        },
+        "outputId": "1502b50b-b9bb-489e-8cc1-59b056494afe"
+      },
+      "source": [
+        "print(\"(Rows, columns): \" + str(data.shape))\n",
+        "data.columns "
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(Rows, columns): (303, 14)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
+              "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PxY79ivL80gB",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 277
+        },
+        "outputId": "0d181b67-b67d-4816-f470-93a0efb77fc1"
+      },
+      "source": [
+        "data.nunique(axis=0)# returns the number of unique values for each variable."
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "age          41\n",
+              "sex           2\n",
+              "cp            4\n",
+              "trestbps     49\n",
+              "chol        152\n",
+              "fbs           2\n",
+              "restecg       3\n",
+              "thalach      91\n",
+              "exang         2\n",
+              "oldpeak      40\n",
+              "slope         3\n",
+              "ca            5\n",
+              "thal          4\n",
+              "target        2\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wn63NFgG88dd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 478
+        },
+        "outputId": "cebe20f1-48e7-48ba-f425-ab5e5061f138"
+      },
+      "source": [
+        "#summarizes the count, mean, standard deviation, min, and max for numeric variables.\n",
+        "data.describe().transpose()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>min</th>\n",
+              "      <th>25%</th>\n",
+              "      <th>50%</th>\n",
+              "      <th>75%</th>\n",
+              "      <th>max</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>age</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>54.366337</td>\n",
+              "      <td>9.082101</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>47.5</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>61.0</td>\n",
+              "      <td>77.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sex</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.683168</td>\n",
+              "      <td>0.466011</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cp</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.966997</td>\n",
+              "      <td>1.032052</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>trestbps</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>131.623762</td>\n",
+              "      <td>17.538143</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>130.0</td>\n",
+              "      <td>140.0</td>\n",
+              "      <td>200.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>chol</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>246.264026</td>\n",
+              "      <td>51.830751</td>\n",
+              "      <td>126.0</td>\n",
+              "      <td>211.0</td>\n",
+              "      <td>240.0</td>\n",
+              "      <td>274.5</td>\n",
+              "      <td>564.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>fbs</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.148515</td>\n",
+              "      <td>0.356198</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>restecg</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.528053</td>\n",
+              "      <td>0.525860</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>thalach</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>149.646865</td>\n",
+              "      <td>22.905161</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>133.5</td>\n",
+              "      <td>153.0</td>\n",
+              "      <td>166.0</td>\n",
+              "      <td>202.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>exang</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.326733</td>\n",
+              "      <td>0.469794</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>oldpeak</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>1.039604</td>\n",
+              "      <td>1.161075</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>6.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>slope</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>1.399340</td>\n",
+              "      <td>0.616226</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ca</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.729373</td>\n",
+              "      <td>1.022606</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>thal</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>2.313531</td>\n",
+              "      <td>0.612277</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.544554</td>\n",
+              "      <td>0.498835</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          count        mean        std    min    25%    50%    75%    max\n",
+              "age       303.0   54.366337   9.082101   29.0   47.5   55.0   61.0   77.0\n",
+              "sex       303.0    0.683168   0.466011    0.0    0.0    1.0    1.0    1.0\n",
+              "cp        303.0    0.966997   1.032052    0.0    0.0    1.0    2.0    3.0\n",
+              "trestbps  303.0  131.623762  17.538143   94.0  120.0  130.0  140.0  200.0\n",
+              "chol      303.0  246.264026  51.830751  126.0  211.0  240.0  274.5  564.0\n",
+              "fbs       303.0    0.148515   0.356198    0.0    0.0    0.0    0.0    1.0\n",
+              "restecg   303.0    0.528053   0.525860    0.0    0.0    1.0    1.0    2.0\n",
+              "thalach   303.0  149.646865  22.905161   71.0  133.5  153.0  166.0  202.0\n",
+              "exang     303.0    0.326733   0.469794    0.0    0.0    0.0    1.0    1.0\n",
+              "oldpeak   303.0    1.039604   1.161075    0.0    0.0    0.8    1.6    6.2\n",
+              "slope     303.0    1.399340   0.616226    0.0    1.0    1.0    2.0    2.0\n",
+              "ca        303.0    0.729373   1.022606    0.0    0.0    0.0    1.0    4.0\n",
+              "thal      303.0    2.313531   0.612277    0.0    2.0    2.0    3.0    3.0\n",
+              "target    303.0    0.544554   0.498835    0.0    0.0    1.0    1.0    1.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "a6ushlSP9EJP",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 277
+        },
+        "outputId": "686cd529-f551-4856-b651-0bef8b688819"
+      },
+      "source": [
+        "# Display the Missing Values\n",
+        "\n",
+        "print(data.isna().sum())"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "age         0\n",
+            "sex         0\n",
+            "cp          0\n",
+            "trestbps    0\n",
+            "chol        0\n",
+            "fbs         0\n",
+            "restecg     0\n",
+            "thalach     0\n",
+            "exang       0\n",
+            "oldpeak     0\n",
+            "slope       0\n",
+            "ca          0\n",
+            "thal        0\n",
+            "target      0\n",
+            "dtype: int64\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IXOMCsIl9JpJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 612
+        },
+        "outputId": "bc94bd22-562c-4124-cfae-f96fce7036b2"
+      },
+      "source": [
+        "# calculate correlation matrix\n",
+        "\n",
+        "corr = data.corr()\n",
+        "plt.subplots(figsize=(15,10))\n",
+        "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, annot=True, cmap=sns.diverging_palette(220, 20, as_cmap=True))\n",
+        "sns.heatmap(corr, xticklabels=corr.columns,\n",
+        "            yticklabels=corr.columns, \n",
+        "            annot=True,\n",
+        "            cmap=sns.diverging_palette(220, 20, as_cmap=True))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb0d9467518>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1080x720 with 3 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qqZ2RHNI9gFK"
+      },
+      "source": [
+        "Filtering data by positive & negative Heart Disease patient"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "n9lROx7X9T2W",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 294
+        },
+        "outputId": "14670672-0759-4aa3-f6d9-7ebe6e6976a9"
+      },
+      "source": [
+        "# Filtering data by POSITIVE Heart Disease patient\n",
+        "pos_data = data[data['target']==1]\n",
+        "pos_data.describe()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>cp</th>\n",
+              "      <th>trestbps</th>\n",
+              "      <th>chol</th>\n",
+              "      <th>fbs</th>\n",
+              "      <th>restecg</th>\n",
+              "      <th>thalach</th>\n",
+              "      <th>exang</th>\n",
+              "      <th>oldpeak</th>\n",
+              "      <th>slope</th>\n",
+              "      <th>ca</th>\n",
+              "      <th>thal</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>52.496970</td>\n",
+              "      <td>0.563636</td>\n",
+              "      <td>1.375758</td>\n",
+              "      <td>129.303030</td>\n",
+              "      <td>242.230303</td>\n",
+              "      <td>0.139394</td>\n",
+              "      <td>0.593939</td>\n",
+              "      <td>158.466667</td>\n",
+              "      <td>0.139394</td>\n",
+              "      <td>0.583030</td>\n",
+              "      <td>1.593939</td>\n",
+              "      <td>0.363636</td>\n",
+              "      <td>2.121212</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>9.550651</td>\n",
+              "      <td>0.497444</td>\n",
+              "      <td>0.952222</td>\n",
+              "      <td>16.169613</td>\n",
+              "      <td>53.552872</td>\n",
+              "      <td>0.347412</td>\n",
+              "      <td>0.504818</td>\n",
+              "      <td>19.174276</td>\n",
+              "      <td>0.347412</td>\n",
+              "      <td>0.780683</td>\n",
+              "      <td>0.593635</td>\n",
+              "      <td>0.848894</td>\n",
+              "      <td>0.465752</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>94.000000</td>\n",
+              "      <td>126.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>96.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>120.000000</td>\n",
+              "      <td>208.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>149.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>52.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>130.000000</td>\n",
+              "      <td>234.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>161.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.200000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>59.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>140.000000</td>\n",
+              "      <td>267.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>172.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>76.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>180.000000</td>\n",
+              "      <td>564.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>202.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>4.200000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>4.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              age         sex          cp  ...          ca        thal  target\n",
+              "count  165.000000  165.000000  165.000000  ...  165.000000  165.000000   165.0\n",
+              "mean    52.496970    0.563636    1.375758  ...    0.363636    2.121212     1.0\n",
+              "std      9.550651    0.497444    0.952222  ...    0.848894    0.465752     0.0\n",
+              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000     1.0\n",
+              "25%     44.000000    0.000000    1.000000  ...    0.000000    2.000000     1.0\n",
+              "50%     52.000000    1.000000    2.000000  ...    0.000000    2.000000     1.0\n",
+              "75%     59.000000    1.000000    2.000000  ...    0.000000    2.000000     1.0\n",
+              "max     76.000000    1.000000    3.000000  ...    4.000000    3.000000     1.0\n",
+              "\n",
+              "[8 rows x 14 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iNdi8cwN9wmU",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 294
+        },
+        "outputId": "67693c50-5cd2-45e4-b4d5-0d136fca57ee"
+      },
+      "source": [
+        "# Filtering data by NEGATIVE Heart Disease patient\n",
+        "pos_data = data[data['target']==0]\n",
+        "pos_data.describe()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>cp</th>\n",
+              "      <th>trestbps</th>\n",
+              "      <th>chol</th>\n",
+              "      <th>fbs</th>\n",
+              "      <th>restecg</th>\n",
+              "      <th>thalach</th>\n",
+              "      <th>exang</th>\n",
+              "      <th>oldpeak</th>\n",
+              "      <th>slope</th>\n",
+              "      <th>ca</th>\n",
+              "      <th>thal</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>56.601449</td>\n",
+              "      <td>0.826087</td>\n",
+              "      <td>0.478261</td>\n",
+              "      <td>134.398551</td>\n",
+              "      <td>251.086957</td>\n",
+              "      <td>0.159420</td>\n",
+              "      <td>0.449275</td>\n",
+              "      <td>139.101449</td>\n",
+              "      <td>0.550725</td>\n",
+              "      <td>1.585507</td>\n",
+              "      <td>1.166667</td>\n",
+              "      <td>1.166667</td>\n",
+              "      <td>2.543478</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>7.962082</td>\n",
+              "      <td>0.380416</td>\n",
+              "      <td>0.905920</td>\n",
+              "      <td>18.729944</td>\n",
+              "      <td>49.454614</td>\n",
+              "      <td>0.367401</td>\n",
+              "      <td>0.541321</td>\n",
+              "      <td>22.598782</td>\n",
+              "      <td>0.499232</td>\n",
+              "      <td>1.300340</td>\n",
+              "      <td>0.561324</td>\n",
+              "      <td>1.043460</td>\n",
+              "      <td>0.684762</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>35.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>131.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>71.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>52.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>120.000000</td>\n",
+              "      <td>217.250000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>125.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.600000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>58.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>130.000000</td>\n",
+              "      <td>249.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>142.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.400000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>62.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>144.750000</td>\n",
+              "      <td>283.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>156.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.500000</td>\n",
+              "      <td>1.750000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>77.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>200.000000</td>\n",
+              "      <td>409.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>195.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>6.200000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>4.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              age         sex          cp  ...          ca        thal  target\n",
+              "count  138.000000  138.000000  138.000000  ...  138.000000  138.000000   138.0\n",
+              "mean    56.601449    0.826087    0.478261  ...    1.166667    2.543478     0.0\n",
+              "std      7.962082    0.380416    0.905920  ...    1.043460    0.684762     0.0\n",
+              "min     35.000000    0.000000    0.000000  ...    0.000000    0.000000     0.0\n",
+              "25%     52.000000    1.000000    0.000000  ...    0.000000    2.000000     0.0\n",
+              "50%     58.000000    1.000000    0.000000  ...    1.000000    3.000000     0.0\n",
+              "75%     62.000000    1.000000    0.000000  ...    2.000000    3.000000     0.0\n",
+              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000     0.0\n",
+              "\n",
+              "[8 rows x 14 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DbzJPYQV9lPu"
+      },
+      "source": [
+        "Assign the 13 features to X, & the last column to our classification predictor, y"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FjMSVs94960R"
+      },
+      "source": [
+        "X = data.iloc[:, :-1].values\n",
+        "y = data.iloc[:, -1].values"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kYwABWTb-AJx"
+      },
+      "source": [
+        "Split: the data set into the Training set and Test set"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DlgSSiYE99tk"
+      },
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_test, y_train, y_test = train_test_split(X,y,test_size = 0.2, random_state = 1)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MuoZklrK-FJY"
+      },
+      "source": [
+        "Normalize: Standardizing the data a mean of 0 and a standard deviation of 1."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rGfWqZir-LLs"
+      },
+      "source": [
+        "from sklearn.preprocessing import StandardScaler\n",
+        "sc = StandardScaler()\n",
+        "x_train = sc.fit_transform(x_train)\n",
+        "x_test = sc.transform(x_test)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CwBqTSrI-WVv"
+      },
+      "source": [
+        "Modeling /Training"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HuFEqvOwUEv2"
+      },
+      "source": [
+        "SVM"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZZuuA6LZUGMh",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 86
+        },
+        "outputId": "fc104267-1538-4ae7-f813-c56acf982ff2"
+      },
+      "source": [
+        "from sklearn.svm import SVC\n",
+        "svm_model = SVC(kernel='rbf', gamma=0.1, C=1.0)\n",
+        "svm_model.fit(x_train, y_train)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
+              "    decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n",
+              "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+              "    tol=0.001, verbose=False)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "N_Wm-g1uUL8a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 433
+        },
+        "outputId": "45d1eee2-3416-4e6a-de87-566a51c15977"
+      },
+      "source": [
+        "print_score(svm_model, x_train, y_train, x_test, y_test, train=True)\n",
+        "print_score(svm_model, x_train, y_train, x_test, y_test, train=False)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Train Result:\n",
+            "================================================\n",
+            "Accuracy Score: 94.63%\n",
+            "_______________________________________________\n",
+            "Classification Report:\tPrecision Score: 93.53%\n",
+            "\t\t\tRecall Score: 97.01%\n",
+            "\t\t\tF1 score: 95.24%\n",
+            "_______________________________________________\n",
+            "Confusion Matrix: \n",
+            " [[ 99   9]\n",
+            " [  4 130]]\n",
+            "\n",
+            "Test Result:\n",
+            "================================================\n",
+            "Accuracy Score: 75.41%\n",
+            "_______________________________________________\n",
+            "Classification Report:\tPrecision Score: 72.22%\n",
+            "\t\t\tRecall Score: 83.87%\n",
+            "\t\t\tF1 score: 77.61%\n",
+            "_______________________________________________\n",
+            "Confusion Matrix: \n",
+            " [[20 10]\n",
+            " [ 5 26]]\n",
+            "\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ux2nH9abUfmP",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 110
+        },
+        "outputId": "0beb292f-8e56-40d4-a462-8e514b55c020"
+      },
+      "source": [
+        "test_score = accuracy_score(y_test, svm_model.predict(x_test)) * 100\n",
+        "train_score = accuracy_score(y_train, svm_model.predict(x_train)) * 100\n",
+        "\n",
+        "results_df = pd.DataFrame(data=[[\"Support Vector Machine\", train_score, test_score]], \n",
+        "                          columns=['Model', 'Training Accuracy %', 'Testing Accuracy %'])\n",
+        "results_df = results_df.append(results_df, ignore_index=True)\n",
+        "results_df"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Model</th>\n",
+              "      <th>Training Accuracy %</th>\n",
+              "      <th>Testing Accuracy %</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Support Vector Machine</td>\n",
+              "      <td>94.628099</td>\n",
+              "      <td>75.409836</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Support Vector Machine</td>\n",
+              "      <td>94.628099</td>\n",
+              "      <td>75.409836</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                    Model  Training Accuracy %  Testing Accuracy %\n",
+              "0  Support Vector Machine            94.628099           75.409836\n",
+              "1  Support Vector Machine            94.628099           75.409836"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 18
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5b-zU0o_XkLa"
+      },
+      "source": [
+        "**TUNED MODELS**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3HzudoDKXvUk"
+      },
+      "source": [
+        "Support Vector Machine Hyperparameter Tuning\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "RyotIOTbXpKw"
+      },
+      "source": [
+        "from sklearn.model_selection import GridSearchCV\n",
+        "\n",
+        "svm_model = SVC(kernel='rbf', gamma=0.1, C=1.0)\n",
+        "\n",
+        "params = {\"C\":(0.1, 0.5, 1, 2, 5, 10, 20), \n",
+        "          \"gamma\":(0.001, 0.01, 0.1, 0.25, 0.5, 0.75, 1), \n",
+        "          \"kernel\":('linear', 'poly', 'rbf')}\n",
+        "\n",
+        "svm_grid = GridSearchCV(svm_model, params, n_jobs=-1, cv=5, verbose=1, scoring=\"accuracy\")"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IFE-CLC2Xe96",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 433
+        },
+        "outputId": "0d282465-96c7-4f34-ced4-d346266ff938"
+      },
+      "source": [
+        "svm_model = SVC(C=5, gamma=0.01, kernel='rbf')\n",
+        "svm_model.fit(x_train, y_train)\n",
+        "\n",
+        "print_score(svm_model, x_train, y_train, x_test, y_test, train=True)\n",
+        "print_score(svm_model, x_train, y_train, x_test, y_test, train=False)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Train Result:\n",
+            "================================================\n",
+            "Accuracy Score: 87.60%\n",
+            "_______________________________________________\n",
+            "Classification Report:\tPrecision Score: 85.62%\n",
+            "\t\t\tRecall Score: 93.28%\n",
+            "\t\t\tF1 score: 89.29%\n",
+            "_______________________________________________\n",
+            "Confusion Matrix: \n",
+            " [[ 87  21]\n",
+            " [  9 125]]\n",
+            "\n",
+            "Test Result:\n",
+            "================================================\n",
+            "Accuracy Score: 75.41%\n",
+            "_______________________________________________\n",
+            "Classification Report:\tPrecision Score: 72.22%\n",
+            "\t\t\tRecall Score: 83.87%\n",
+            "\t\t\tF1 score: 77.61%\n",
+            "_______________________________________________\n",
+            "Confusion Matrix: \n",
+            " [[20 10]\n",
+            " [ 5 26]]\n",
+            "\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "enx2NHZdYayj",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "outputId": "f99fe4cb-70d2-4da6-96ea-026696bc9dac"
+      },
+      "source": [
+        "test_score = accuracy_score(y_test, svm_model.predict(x_test)) * 100\n",
+        "train_score = accuracy_score(y_train, svm_model.predict(x_train)) * 100\n",
+        "\n",
+        "tuning_results_df = pd.DataFrame(data=[[\"Tuned Support Vector Machine\", train_score, test_score]], \n",
+        "                          columns=['Model', 'Training Accuracy %', 'Testing Accuracy %'])\n",
+        "tuning_results_df"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Model</th>\n",
+              "      <th>Training Accuracy %</th>\n",
+              "      <th>Testing Accuracy %</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Tuned Support Vector Machine</td>\n",
+              "      <td>87.603306</td>\n",
+              "      <td>75.409836</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                          Model  Training Accuracy %  Testing Accuracy %\n",
+              "0  Tuned Support Vector Machine            87.603306           75.409836"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 27
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Data Science/dataset.csv b/Data Science/dataset.csv
new file mode 100644
index 0000000..1939fc6
--- /dev/null
+++ b/Data Science/dataset.csv	
@@ -0,0 +1,304 @@
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+47,1,0,110,275,0,0,118,1,1,1,1,2,0
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+42,1,0,136,315,0,1,125,1,1.8,1,0,1,0
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+45,1,3,110,264,0,1,132,0,1.2,1,0,3,0
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+57,1,0,130,131,0,1,115,1,1.2,1,1,3,0
+57,0,1,130,236,0,0,174,0,0,1,1,2,0

From 2a79468d90ccde0312b2b8af4e5a1a7dd7558abe Mon Sep 17 00:00:00 2001
From: Kainat-Mahar <72600530+Kainat-Mahar@users.noreply.github.com>
Date: Fri, 22 Oct 2021 13:32:44 +0500
Subject: [PATCH 057/232] Delete Heart Disease Predictions

---
 Data Science/Heart Disease Predictions | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 Data Science/Heart Disease Predictions

diff --git a/Data Science/Heart Disease Predictions b/Data Science/Heart Disease Predictions
deleted file mode 100644
index 8b13789..0000000
--- a/Data Science/Heart Disease Predictions	
+++ /dev/null
@@ -1 +0,0 @@
-

From f2f7f57a84fca10d187133fd2f143dfe95270b7a Mon Sep 17 00:00:00 2001
From: Kainat-Mahar <72600530+Kainat-Mahar@users.noreply.github.com>
Date: Fri, 22 Oct 2021 14:10:47 +0500
Subject: [PATCH 058/232] Add files via upload

---
 Data Science/DSA_Project_on_KNN.ipynb | 1302 +++++++++++++++++++++++++
 1 file changed, 1302 insertions(+)
 create mode 100644 Data Science/DSA_Project_on_KNN.ipynb

diff --git a/Data Science/DSA_Project_on_KNN.ipynb b/Data Science/DSA_Project_on_KNN.ipynb
new file mode 100644
index 0000000..de9a3f8
--- /dev/null
+++ b/Data Science/DSA_Project_on_KNN.ipynb	
@@ -0,0 +1,1302 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "DSA_Project on KNN.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HbbGwEA47kDj"
+      },
+      "source": [
+        "Importing Libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "roYk-Q3s6yVd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 72
+        },
+        "outputId": "b8676cc9-bad4-4aae-faf1-b2d77076f0bc"
+      },
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib as plt\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
+            "  import pandas.util.testing as tm\n"
+          ],
+          "name": "stderr"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7v8I08N58jcA"
+      },
+      "source": [
+        "Data Wrangling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ttq0PCrZ8i_d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 197
+        },
+        "outputId": "9d02d850-01ce-443f-e093-96dc6516e825"
+      },
+      "source": [
+        "data = pd.read_csv('dataset.csv')\n",
+        "\n",
+        "data.head(5)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>cp</th>\n",
+              "      <th>trestbps</th>\n",
+              "      <th>chol</th>\n",
+              "      <th>fbs</th>\n",
+              "      <th>restecg</th>\n",
+              "      <th>thalach</th>\n",
+              "      <th>exang</th>\n",
+              "      <th>oldpeak</th>\n",
+              "      <th>slope</th>\n",
+              "      <th>ca</th>\n",
+              "      <th>thal</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>63</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>145</td>\n",
+              "      <td>233</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>150</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>37</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>130</td>\n",
+              "      <td>250</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>187</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>41</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>130</td>\n",
+              "      <td>204</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>172</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>56</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>120</td>\n",
+              "      <td>236</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>178</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>57</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>120</td>\n",
+              "      <td>354</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>163</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
+              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
+              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
+              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
+              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
+              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
+              "\n",
+              "[5 rows x 14 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8PJwi9rc8tp-",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 87
+        },
+        "outputId": "162e9962-f103-495f-dadb-b99ca69d4724"
+      },
+      "source": [
+        "print(\"(Rows, columns): \" + str(data.shape))\n",
+        "data.columns "
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(Rows, columns): (303, 14)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
+              "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PxY79ivL80gB",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "outputId": "bd6ed805-91a5-449f-d065-2d71b4b53924"
+      },
+      "source": [
+        "data.nunique(axis=0)# returns the number of unique values for each variable."
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "age          41\n",
+              "sex           2\n",
+              "cp            4\n",
+              "trestbps     49\n",
+              "chol        152\n",
+              "fbs           2\n",
+              "restecg       3\n",
+              "thalach      91\n",
+              "exang         2\n",
+              "oldpeak      40\n",
+              "slope         3\n",
+              "ca            5\n",
+              "thal          4\n",
+              "target        2\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wn63NFgG88dd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 467
+        },
+        "outputId": "80187cdd-c809-4a94-96a1-2822be8c7844"
+      },
+      "source": [
+        "#summarizes the count, mean, standard deviation, min, and max for numeric variables.\n",
+        "data.describe().transpose()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>min</th>\n",
+              "      <th>25%</th>\n",
+              "      <th>50%</th>\n",
+              "      <th>75%</th>\n",
+              "      <th>max</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>age</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>54.366337</td>\n",
+              "      <td>9.082101</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>47.5</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>61.0</td>\n",
+              "      <td>77.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sex</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.683168</td>\n",
+              "      <td>0.466011</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cp</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.966997</td>\n",
+              "      <td>1.032052</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>trestbps</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>131.623762</td>\n",
+              "      <td>17.538143</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>130.0</td>\n",
+              "      <td>140.0</td>\n",
+              "      <td>200.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>chol</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>246.264026</td>\n",
+              "      <td>51.830751</td>\n",
+              "      <td>126.0</td>\n",
+              "      <td>211.0</td>\n",
+              "      <td>240.0</td>\n",
+              "      <td>274.5</td>\n",
+              "      <td>564.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>fbs</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.148515</td>\n",
+              "      <td>0.356198</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>restecg</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.528053</td>\n",
+              "      <td>0.525860</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>thalach</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>149.646865</td>\n",
+              "      <td>22.905161</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>133.5</td>\n",
+              "      <td>153.0</td>\n",
+              "      <td>166.0</td>\n",
+              "      <td>202.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>exang</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.326733</td>\n",
+              "      <td>0.469794</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>oldpeak</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>1.039604</td>\n",
+              "      <td>1.161075</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>6.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>slope</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>1.399340</td>\n",
+              "      <td>0.616226</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ca</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.729373</td>\n",
+              "      <td>1.022606</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>thal</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>2.313531</td>\n",
+              "      <td>0.612277</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.544554</td>\n",
+              "      <td>0.498835</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          count        mean        std    min    25%    50%    75%    max\n",
+              "age       303.0   54.366337   9.082101   29.0   47.5   55.0   61.0   77.0\n",
+              "sex       303.0    0.683168   0.466011    0.0    0.0    1.0    1.0    1.0\n",
+              "cp        303.0    0.966997   1.032052    0.0    0.0    1.0    2.0    3.0\n",
+              "trestbps  303.0  131.623762  17.538143   94.0  120.0  130.0  140.0  200.0\n",
+              "chol      303.0  246.264026  51.830751  126.0  211.0  240.0  274.5  564.0\n",
+              "fbs       303.0    0.148515   0.356198    0.0    0.0    0.0    0.0    1.0\n",
+              "restecg   303.0    0.528053   0.525860    0.0    0.0    1.0    1.0    2.0\n",
+              "thalach   303.0  149.646865  22.905161   71.0  133.5  153.0  166.0  202.0\n",
+              "exang     303.0    0.326733   0.469794    0.0    0.0    0.0    1.0    1.0\n",
+              "oldpeak   303.0    1.039604   1.161075    0.0    0.0    0.8    1.6    6.2\n",
+              "slope     303.0    1.399340   0.616226    0.0    1.0    1.0    2.0    2.0\n",
+              "ca        303.0    0.729373   1.022606    0.0    0.0    0.0    1.0    4.0\n",
+              "thal      303.0    2.313531   0.612277    0.0    2.0    2.0    3.0    3.0\n",
+              "target    303.0    0.544554   0.498835    0.0    0.0    1.0    1.0    1.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "a6ushlSP9EJP",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "outputId": "b75d5826-d26f-4ffa-a377-bd300bb3866e"
+      },
+      "source": [
+        "# Display the Missing Values\n",
+        "\n",
+        "print(data.isna().sum())"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "age         0\n",
+            "sex         0\n",
+            "cp          0\n",
+            "trestbps    0\n",
+            "chol        0\n",
+            "fbs         0\n",
+            "restecg     0\n",
+            "thalach     0\n",
+            "exang       0\n",
+            "oldpeak     0\n",
+            "slope       0\n",
+            "ca          0\n",
+            "thal        0\n",
+            "target      0\n",
+            "dtype: int64\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IXOMCsIl9JpJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 612
+        },
+        "outputId": "f43d391b-a702-4d0e-9d42-98170d63d722"
+      },
+      "source": [
+        "# calculate correlation matrix\n",
+        "\n",
+        "corr = data.corr()\n",
+        "plt.subplots(figsize=(15,10))\n",
+        "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, annot=True, cmap=sns.diverging_palette(220, 20, as_cmap=True))\n",
+        "sns.heatmap(corr, xticklabels=corr.columns,\n",
+        "            yticklabels=corr.columns, \n",
+        "            annot=True,\n",
+        "            cmap=sns.diverging_palette(220, 20, as_cmap=True))"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7feccbdf2278>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1080x720 with 3 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qqZ2RHNI9gFK"
+      },
+      "source": [
+        "Filtering data by positive & negative Heart Disease patient"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "n9lROx7X9T2W",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 287
+        },
+        "outputId": "1e46959c-7449-4721-a272-7a218c221764"
+      },
+      "source": [
+        "# Filtering data by POSITIVE Heart Disease patient\n",
+        "pos_data = data[data['target']==1]\n",
+        "pos_data.describe()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>cp</th>\n",
+              "      <th>trestbps</th>\n",
+              "      <th>chol</th>\n",
+              "      <th>fbs</th>\n",
+              "      <th>restecg</th>\n",
+              "      <th>thalach</th>\n",
+              "      <th>exang</th>\n",
+              "      <th>oldpeak</th>\n",
+              "      <th>slope</th>\n",
+              "      <th>ca</th>\n",
+              "      <th>thal</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>52.496970</td>\n",
+              "      <td>0.563636</td>\n",
+              "      <td>1.375758</td>\n",
+              "      <td>129.303030</td>\n",
+              "      <td>242.230303</td>\n",
+              "      <td>0.139394</td>\n",
+              "      <td>0.593939</td>\n",
+              "      <td>158.466667</td>\n",
+              "      <td>0.139394</td>\n",
+              "      <td>0.583030</td>\n",
+              "      <td>1.593939</td>\n",
+              "      <td>0.363636</td>\n",
+              "      <td>2.121212</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>9.550651</td>\n",
+              "      <td>0.497444</td>\n",
+              "      <td>0.952222</td>\n",
+              "      <td>16.169613</td>\n",
+              "      <td>53.552872</td>\n",
+              "      <td>0.347412</td>\n",
+              "      <td>0.504818</td>\n",
+              "      <td>19.174276</td>\n",
+              "      <td>0.347412</td>\n",
+              "      <td>0.780683</td>\n",
+              "      <td>0.593635</td>\n",
+              "      <td>0.848894</td>\n",
+              "      <td>0.465752</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>94.000000</td>\n",
+              "      <td>126.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>96.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>120.000000</td>\n",
+              "      <td>208.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>149.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>52.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>130.000000</td>\n",
+              "      <td>234.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>161.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.200000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>59.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>140.000000</td>\n",
+              "      <td>267.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>172.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>76.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>180.000000</td>\n",
+              "      <td>564.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>202.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>4.200000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>4.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              age         sex          cp  ...          ca        thal  target\n",
+              "count  165.000000  165.000000  165.000000  ...  165.000000  165.000000   165.0\n",
+              "mean    52.496970    0.563636    1.375758  ...    0.363636    2.121212     1.0\n",
+              "std      9.550651    0.497444    0.952222  ...    0.848894    0.465752     0.0\n",
+              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000     1.0\n",
+              "25%     44.000000    0.000000    1.000000  ...    0.000000    2.000000     1.0\n",
+              "50%     52.000000    1.000000    2.000000  ...    0.000000    2.000000     1.0\n",
+              "75%     59.000000    1.000000    2.000000  ...    0.000000    2.000000     1.0\n",
+              "max     76.000000    1.000000    3.000000  ...    4.000000    3.000000     1.0\n",
+              "\n",
+              "[8 rows x 14 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iNdi8cwN9wmU",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 287
+        },
+        "outputId": "b67f565d-7e05-4f7e-ac88-bac6c15ca709"
+      },
+      "source": [
+        "# Filtering data by NEGATIVE Heart Disease patient\n",
+        "pos_data = data[data['target']==0]\n",
+        "pos_data.describe()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>cp</th>\n",
+              "      <th>trestbps</th>\n",
+              "      <th>chol</th>\n",
+              "      <th>fbs</th>\n",
+              "      <th>restecg</th>\n",
+              "      <th>thalach</th>\n",
+              "      <th>exang</th>\n",
+              "      <th>oldpeak</th>\n",
+              "      <th>slope</th>\n",
+              "      <th>ca</th>\n",
+              "      <th>thal</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>56.601449</td>\n",
+              "      <td>0.826087</td>\n",
+              "      <td>0.478261</td>\n",
+              "      <td>134.398551</td>\n",
+              "      <td>251.086957</td>\n",
+              "      <td>0.159420</td>\n",
+              "      <td>0.449275</td>\n",
+              "      <td>139.101449</td>\n",
+              "      <td>0.550725</td>\n",
+              "      <td>1.585507</td>\n",
+              "      <td>1.166667</td>\n",
+              "      <td>1.166667</td>\n",
+              "      <td>2.543478</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>7.962082</td>\n",
+              "      <td>0.380416</td>\n",
+              "      <td>0.905920</td>\n",
+              "      <td>18.729944</td>\n",
+              "      <td>49.454614</td>\n",
+              "      <td>0.367401</td>\n",
+              "      <td>0.541321</td>\n",
+              "      <td>22.598782</td>\n",
+              "      <td>0.499232</td>\n",
+              "      <td>1.300340</td>\n",
+              "      <td>0.561324</td>\n",
+              "      <td>1.043460</td>\n",
+              "      <td>0.684762</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>35.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>131.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>71.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>52.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>120.000000</td>\n",
+              "      <td>217.250000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>125.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.600000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>58.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>130.000000</td>\n",
+              "      <td>249.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>142.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.400000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>62.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>144.750000</td>\n",
+              "      <td>283.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>156.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.500000</td>\n",
+              "      <td>1.750000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>77.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>200.000000</td>\n",
+              "      <td>409.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>195.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>6.200000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>4.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              age         sex          cp  ...          ca        thal  target\n",
+              "count  138.000000  138.000000  138.000000  ...  138.000000  138.000000   138.0\n",
+              "mean    56.601449    0.826087    0.478261  ...    1.166667    2.543478     0.0\n",
+              "std      7.962082    0.380416    0.905920  ...    1.043460    0.684762     0.0\n",
+              "min     35.000000    0.000000    0.000000  ...    0.000000    0.000000     0.0\n",
+              "25%     52.000000    1.000000    0.000000  ...    0.000000    2.000000     0.0\n",
+              "50%     58.000000    1.000000    0.000000  ...    1.000000    3.000000     0.0\n",
+              "75%     62.000000    1.000000    0.000000  ...    2.000000    3.000000     0.0\n",
+              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000     0.0\n",
+              "\n",
+              "[8 rows x 14 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DbzJPYQV9lPu"
+      },
+      "source": [
+        "Assign the 13 features to X, & the last column to our classification predictor, y"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FjMSVs94960R"
+      },
+      "source": [
+        "X = data.iloc[:, :-1].values\n",
+        "y = data.iloc[:, -1].values"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kYwABWTb-AJx"
+      },
+      "source": [
+        "Split: the data set into the Training set and Test set"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DlgSSiYE99tk"
+      },
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_test, y_train, y_test = train_test_split(X,y,test_size = 0.2, random_state = 1)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MuoZklrK-FJY"
+      },
+      "source": [
+        "Normalize: Standardizing the data a mean of 0 and a standard deviation of 1."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rGfWqZir-LLs"
+      },
+      "source": [
+        "from sklearn.preprocessing import StandardScaler\n",
+        "sc = StandardScaler()\n",
+        "x_train = sc.fit_transform(x_train)\n",
+        "x_test = sc.transform(x_test)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CwBqTSrI-WVv"
+      },
+      "source": [
+        "Modeling /Training"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Pxb_vKN2-dmc"
+      },
+      "source": [
+        "Model 1: K-NN (K-Nearest Neighbors)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "WwTHdnS2-Ysh",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 176
+        },
+        "outputId": "80e3157c-dddc-44ab-fb76-8508ec3ae969"
+      },
+      "source": [
+        "from sklearn.metrics import classification_report \n",
+        "from sklearn.neighbors import KNeighborsClassifier\n",
+        "\n",
+        "model1 = KNeighborsClassifier() # get instance of model\n",
+        "model1.fit(x_train, y_train) # Train/Fit model \n",
+        "\n",
+        "y_pred1 = model1.predict(x_test) # get y predictions\n",
+        "print(classification_report(y_test, y_pred1)) # output accuracy"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.78      0.70      0.74        30\n",
+            "           1       0.74      0.81      0.77        31\n",
+            "\n",
+            "    accuracy                           0.75        61\n",
+            "   macro avg       0.76      0.75      0.75        61\n",
+            "weighted avg       0.76      0.75      0.75        61\n",
+            "\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7HeMf82L_l2M"
+      },
+      "source": [
+        "Making the Confusion Matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5Nrcd6au_dw0",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 70
+        },
+        "outputId": "bdb6872f-218c-486f-b581-4614bdbfbac5"
+      },
+      "source": [
+        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+        "cm = confusion_matrix(y_test, y_pred1)\n",
+        "print(cm)\n",
+        "accuracy_score(y_test, y_pred1)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[[21  9]\n",
+            " [ 6 25]]\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.7540983606557377"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NUwd6OzI2BNa",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "5236745f-7860-4ef6-b548-77172b9affc2"
+      },
+      "source": [
+        "TN = cm[0][0]\n",
+        "FN = cm[1][0]\n",
+        "TP = cm[1][1]\n",
+        "FP = cm[0][1]\n",
+        "\n",
+        "specificity = (TN/TN+FP)\n",
+        "\n",
+        "print ('Specificity = ', specificity)\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Specificity =  10.0\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "v7q04OW45jVn",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "b91a4392-c89b-435a-dc11-6c086b1ea39d"
+      },
+      "source": [
+        "senstivity = (TP/TP+FN)\n",
+        "\n",
+        "print ('Senstivity = ', senstivity)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Senstivity =  7.0\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From e33ee4b598ae3cb45e57ffe7da699aa4e7f791b6 Mon Sep 17 00:00:00 2001
From: Kainat-Mahar <72600530+Kainat-Mahar@users.noreply.github.com>
Date: Fri, 22 Oct 2021 14:13:25 +0500
Subject: [PATCH 059/232] Add files via upload

---
 ...cation_using_Random_Forest_Algorithm.ipynb | 1355 +++++++++++++++++
 1 file changed, 1355 insertions(+)
 create mode 100644 Data Science/Classification_using_Random_Forest_Algorithm.ipynb

diff --git a/Data Science/Classification_using_Random_Forest_Algorithm.ipynb b/Data Science/Classification_using_Random_Forest_Algorithm.ipynb
new file mode 100644
index 0000000..ef5e38c
--- /dev/null
+++ b/Data Science/Classification_using_Random_Forest_Algorithm.ipynb	
@@ -0,0 +1,1355 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Classification using Random Forest Algorithm.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HbbGwEA47kDj"
+      },
+      "source": [
+        "Importing Libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "roYk-Q3s6yVd"
+      },
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib as plt\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7v8I08N58jcA"
+      },
+      "source": [
+        "Data Wrangling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ttq0PCrZ8i_d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 202
+        },
+        "outputId": "d2a6d459-e354-475f-d2c5-ef08982f82d0"
+      },
+      "source": [
+        "data = pd.read_csv('dataset.csv')\n",
+        "\n",
+        "data.head(5)"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>cp</th>\n",
+              "      <th>trestbps</th>\n",
+              "      <th>chol</th>\n",
+              "      <th>fbs</th>\n",
+              "      <th>restecg</th>\n",
+              "      <th>thalach</th>\n",
+              "      <th>exang</th>\n",
+              "      <th>oldpeak</th>\n",
+              "      <th>slope</th>\n",
+              "      <th>ca</th>\n",
+              "      <th>thal</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>63</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>145</td>\n",
+              "      <td>233</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>150</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>37</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>130</td>\n",
+              "      <td>250</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>187</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>41</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>130</td>\n",
+              "      <td>204</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>172</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>56</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>120</td>\n",
+              "      <td>236</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>178</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>57</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>120</td>\n",
+              "      <td>354</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>163</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
+              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
+              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
+              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
+              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
+              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
+              "\n",
+              "[5 rows x 14 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8PJwi9rc8tp-",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4e8f191b-6599-496e-b97b-8fcbedbc7b22"
+      },
+      "source": [
+        "print(\"(Rows, columns): \" + str(data.shape))\n",
+        "data.columns "
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(Rows, columns): (303, 14)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
+              "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PxY79ivL80gB",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "73b9d73d-e643-4602-ac52-c8df5d5a8413"
+      },
+      "source": [
+        "data.nunique(axis=0)# returns the number of unique values for each variable."
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "age          41\n",
+              "sex           2\n",
+              "cp            4\n",
+              "trestbps     49\n",
+              "chol        152\n",
+              "fbs           2\n",
+              "restecg       3\n",
+              "thalach      91\n",
+              "exang         2\n",
+              "oldpeak      40\n",
+              "slope         3\n",
+              "ca            5\n",
+              "thal          4\n",
+              "target        2\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wn63NFgG88dd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 477
+        },
+        "outputId": "83fe2164-71d8-438a-f38f-6076eb0512c6"
+      },
+      "source": [
+        "#summarizes the count, mean, standard deviation, min, and max for numeric variables.\n",
+        "data.describe().transpose()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>min</th>\n",
+              "      <th>25%</th>\n",
+              "      <th>50%</th>\n",
+              "      <th>75%</th>\n",
+              "      <th>max</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>age</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>54.366337</td>\n",
+              "      <td>9.082101</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>47.5</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>61.0</td>\n",
+              "      <td>77.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sex</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.683168</td>\n",
+              "      <td>0.466011</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cp</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.966997</td>\n",
+              "      <td>1.032052</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>trestbps</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>131.623762</td>\n",
+              "      <td>17.538143</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>130.0</td>\n",
+              "      <td>140.0</td>\n",
+              "      <td>200.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>chol</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>246.264026</td>\n",
+              "      <td>51.830751</td>\n",
+              "      <td>126.0</td>\n",
+              "      <td>211.0</td>\n",
+              "      <td>240.0</td>\n",
+              "      <td>274.5</td>\n",
+              "      <td>564.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>fbs</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.148515</td>\n",
+              "      <td>0.356198</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>restecg</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.528053</td>\n",
+              "      <td>0.525860</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>thalach</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>149.646865</td>\n",
+              "      <td>22.905161</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>133.5</td>\n",
+              "      <td>153.0</td>\n",
+              "      <td>166.0</td>\n",
+              "      <td>202.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>exang</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.326733</td>\n",
+              "      <td>0.469794</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>oldpeak</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>1.039604</td>\n",
+              "      <td>1.161075</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>6.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>slope</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>1.399340</td>\n",
+              "      <td>0.616226</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ca</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.729373</td>\n",
+              "      <td>1.022606</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>thal</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>2.313531</td>\n",
+              "      <td>0.612277</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.544554</td>\n",
+              "      <td>0.498835</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          count        mean        std    min    25%    50%    75%    max\n",
+              "age       303.0   54.366337   9.082101   29.0   47.5   55.0   61.0   77.0\n",
+              "sex       303.0    0.683168   0.466011    0.0    0.0    1.0    1.0    1.0\n",
+              "cp        303.0    0.966997   1.032052    0.0    0.0    1.0    2.0    3.0\n",
+              "trestbps  303.0  131.623762  17.538143   94.0  120.0  130.0  140.0  200.0\n",
+              "chol      303.0  246.264026  51.830751  126.0  211.0  240.0  274.5  564.0\n",
+              "fbs       303.0    0.148515   0.356198    0.0    0.0    0.0    0.0    1.0\n",
+              "restecg   303.0    0.528053   0.525860    0.0    0.0    1.0    1.0    2.0\n",
+              "thalach   303.0  149.646865  22.905161   71.0  133.5  153.0  166.0  202.0\n",
+              "exang     303.0    0.326733   0.469794    0.0    0.0    0.0    1.0    1.0\n",
+              "oldpeak   303.0    1.039604   1.161075    0.0    0.0    0.8    1.6    6.2\n",
+              "slope     303.0    1.399340   0.616226    0.0    1.0    1.0    2.0    2.0\n",
+              "ca        303.0    0.729373   1.022606    0.0    0.0    0.0    1.0    4.0\n",
+              "thal      303.0    2.313531   0.612277    0.0    2.0    2.0    3.0    3.0\n",
+              "target    303.0    0.544554   0.498835    0.0    0.0    1.0    1.0    1.0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "a6ushlSP9EJP",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "366a429a-9d57-4ea8-bc8d-b51b0baf45fc"
+      },
+      "source": [
+        "# Display the Missing Values\n",
+        "\n",
+        "print(data.isna().sum())"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "age         0\n",
+            "sex         0\n",
+            "cp          0\n",
+            "trestbps    0\n",
+            "chol        0\n",
+            "fbs         0\n",
+            "restecg     0\n",
+            "thalach     0\n",
+            "exang       0\n",
+            "oldpeak     0\n",
+            "slope       0\n",
+            "ca          0\n",
+            "thal        0\n",
+            "target      0\n",
+            "dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IXOMCsIl9JpJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 612
+        },
+        "outputId": "7b01ed95-db2a-435c-f6e5-c8423976082c"
+      },
+      "source": [
+        "# calculate correlation matrix\n",
+        "\n",
+        "corr = data.corr()\n",
+        "plt.subplots(figsize=(15,10))\n",
+        "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, annot=True, cmap=sns.diverging_palette(220, 20, as_cmap=True))\n",
+        "sns.heatmap(corr, xticklabels=corr.columns,\n",
+        "            yticklabels=corr.columns, \n",
+        "            annot=True,\n",
+        "            cmap=sns.diverging_palette(220, 20, as_cmap=True))"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c953e9d10>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1080x720 with 3 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qqZ2RHNI9gFK"
+      },
+      "source": [
+        "Filtering data by positive & negative Heart Disease patient"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "n9lROx7X9T2W",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 314
+        },
+        "outputId": "ca94464a-e45e-4429-d316-efebe625f5d0"
+      },
+      "source": [
+        "# Filtering data by POSITIVE Heart Disease patient\n",
+        "pos_data = data[data['target']==1]\n",
+        "pos_data.describe()"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>cp</th>\n",
+              "      <th>trestbps</th>\n",
+              "      <th>chol</th>\n",
+              "      <th>fbs</th>\n",
+              "      <th>restecg</th>\n",
+              "      <th>thalach</th>\n",
+              "      <th>exang</th>\n",
+              "      <th>oldpeak</th>\n",
+              "      <th>slope</th>\n",
+              "      <th>ca</th>\n",
+              "      <th>thal</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>52.496970</td>\n",
+              "      <td>0.563636</td>\n",
+              "      <td>1.375758</td>\n",
+              "      <td>129.303030</td>\n",
+              "      <td>242.230303</td>\n",
+              "      <td>0.139394</td>\n",
+              "      <td>0.593939</td>\n",
+              "      <td>158.466667</td>\n",
+              "      <td>0.139394</td>\n",
+              "      <td>0.583030</td>\n",
+              "      <td>1.593939</td>\n",
+              "      <td>0.363636</td>\n",
+              "      <td>2.121212</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>9.550651</td>\n",
+              "      <td>0.497444</td>\n",
+              "      <td>0.952222</td>\n",
+              "      <td>16.169613</td>\n",
+              "      <td>53.552872</td>\n",
+              "      <td>0.347412</td>\n",
+              "      <td>0.504818</td>\n",
+              "      <td>19.174276</td>\n",
+              "      <td>0.347412</td>\n",
+              "      <td>0.780683</td>\n",
+              "      <td>0.593635</td>\n",
+              "      <td>0.848894</td>\n",
+              "      <td>0.465752</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>94.000000</td>\n",
+              "      <td>126.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>96.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>120.000000</td>\n",
+              "      <td>208.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>149.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>52.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>130.000000</td>\n",
+              "      <td>234.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>161.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.200000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>59.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>140.000000</td>\n",
+              "      <td>267.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>172.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>76.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>180.000000</td>\n",
+              "      <td>564.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>202.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>4.200000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>4.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              age         sex          cp  ...          ca        thal  target\n",
+              "count  165.000000  165.000000  165.000000  ...  165.000000  165.000000   165.0\n",
+              "mean    52.496970    0.563636    1.375758  ...    0.363636    2.121212     1.0\n",
+              "std      9.550651    0.497444    0.952222  ...    0.848894    0.465752     0.0\n",
+              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000     1.0\n",
+              "25%     44.000000    0.000000    1.000000  ...    0.000000    2.000000     1.0\n",
+              "50%     52.000000    1.000000    2.000000  ...    0.000000    2.000000     1.0\n",
+              "75%     59.000000    1.000000    2.000000  ...    0.000000    2.000000     1.0\n",
+              "max     76.000000    1.000000    3.000000  ...    4.000000    3.000000     1.0\n",
+              "\n",
+              "[8 rows x 14 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iNdi8cwN9wmU",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 314
+        },
+        "outputId": "1e819b7f-f8cc-4418-f4f7-d5c8c010c77e"
+      },
+      "source": [
+        "# Filtering data by NEGATIVE Heart Disease patient\n",
+        "pos_data = data[data['target']==0]\n",
+        "pos_data.describe()"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>cp</th>\n",
+              "      <th>trestbps</th>\n",
+              "      <th>chol</th>\n",
+              "      <th>fbs</th>\n",
+              "      <th>restecg</th>\n",
+              "      <th>thalach</th>\n",
+              "      <th>exang</th>\n",
+              "      <th>oldpeak</th>\n",
+              "      <th>slope</th>\n",
+              "      <th>ca</th>\n",
+              "      <th>thal</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>56.601449</td>\n",
+              "      <td>0.826087</td>\n",
+              "      <td>0.478261</td>\n",
+              "      <td>134.398551</td>\n",
+              "      <td>251.086957</td>\n",
+              "      <td>0.159420</td>\n",
+              "      <td>0.449275</td>\n",
+              "      <td>139.101449</td>\n",
+              "      <td>0.550725</td>\n",
+              "      <td>1.585507</td>\n",
+              "      <td>1.166667</td>\n",
+              "      <td>1.166667</td>\n",
+              "      <td>2.543478</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>7.962082</td>\n",
+              "      <td>0.380416</td>\n",
+              "      <td>0.905920</td>\n",
+              "      <td>18.729944</td>\n",
+              "      <td>49.454614</td>\n",
+              "      <td>0.367401</td>\n",
+              "      <td>0.541321</td>\n",
+              "      <td>22.598782</td>\n",
+              "      <td>0.499232</td>\n",
+              "      <td>1.300340</td>\n",
+              "      <td>0.561324</td>\n",
+              "      <td>1.043460</td>\n",
+              "      <td>0.684762</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>35.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>131.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>71.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>52.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>120.000000</td>\n",
+              "      <td>217.250000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>125.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.600000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>58.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>130.000000</td>\n",
+              "      <td>249.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>142.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.400000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>62.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>144.750000</td>\n",
+              "      <td>283.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>156.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.500000</td>\n",
+              "      <td>1.750000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>77.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>200.000000</td>\n",
+              "      <td>409.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>195.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>6.200000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>4.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              age         sex          cp  ...          ca        thal  target\n",
+              "count  138.000000  138.000000  138.000000  ...  138.000000  138.000000   138.0\n",
+              "mean    56.601449    0.826087    0.478261  ...    1.166667    2.543478     0.0\n",
+              "std      7.962082    0.380416    0.905920  ...    1.043460    0.684762     0.0\n",
+              "min     35.000000    0.000000    0.000000  ...    0.000000    0.000000     0.0\n",
+              "25%     52.000000    1.000000    0.000000  ...    0.000000    2.000000     0.0\n",
+              "50%     58.000000    1.000000    0.000000  ...    1.000000    3.000000     0.0\n",
+              "75%     62.000000    1.000000    0.000000  ...    2.000000    3.000000     0.0\n",
+              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000     0.0\n",
+              "\n",
+              "[8 rows x 14 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DbzJPYQV9lPu"
+      },
+      "source": [
+        "Assign the 13 features to X, & the last column to our classification predictor, y"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FjMSVs94960R"
+      },
+      "source": [
+        "X = data.iloc[:, :-1].values\n",
+        "y = data.iloc[:, -1].values"
+      ],
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kYwABWTb-AJx"
+      },
+      "source": [
+        "Split: the data set into the Training set and Test set"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DlgSSiYE99tk"
+      },
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_test, y_train, y_test = train_test_split(X,y,test_size = 0.2, random_state = 1)"
+      ],
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MuoZklrK-FJY"
+      },
+      "source": [
+        "Normalize: Standardizing the data a mean of 0 and a standard deviation of 1."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rGfWqZir-LLs"
+      },
+      "source": [
+        "from sklearn.preprocessing import StandardScaler\n",
+        "sc = StandardScaler()\n",
+        "x_train = sc.fit_transform(x_train)\n",
+        "x_test = sc.transform(x_test)"
+      ],
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CwBqTSrI-WVv"
+      },
+      "source": [
+        "Modeling /Training"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xk0Uem6P-qgq"
+      },
+      "source": [
+        "Model: Random Forest\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "JaSaSKDT-qE-",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bbcc9de5-0a3e-4a70-9eb9-3530cc7803e0"
+      },
+      "source": [
+        "from sklearn.metrics import classification_report \n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "\n",
+        "model3 = RandomForestClassifier(random_state=1)# get instance of model\n",
+        "model3.fit(x_train, y_train) # Train/Fit model \n",
+        "\n",
+        "y_pred3 = model3.predict(x_test) # get y predictions\n",
+        "print(classification_report(y_test, y_pred3)) # output accuracy"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.88      0.70      0.78        30\n",
+            "           1       0.76      0.90      0.82        31\n",
+            "\n",
+            "    accuracy                           0.80        61\n",
+            "   macro avg       0.82      0.80      0.80        61\n",
+            "weighted avg       0.81      0.80      0.80        61\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NYkRl2Et-1vo"
+      },
+      "source": [
+        "Making the Confusion Matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qH0hRD8j-3m9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f8f38c70-ce4c-4824-9ba3-13d9e6b06f5b"
+      },
+      "source": [
+        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+        "cm = confusion_matrix(y_test, y_pred3)\n",
+        "print(cm)\n",
+        "accuracy_score(y_test, y_pred3)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[21  9]\n",
+            " [ 3 28]]\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8032786885245902"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fu7KQOnjzY6g",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "87984799-bb19-4075-de5c-d83033940f4c"
+      },
+      "source": [
+        "TN = cm[0][0]\n",
+        "FN = cm[1][0]\n",
+        "TP = cm[1][1]\n",
+        "FP = cm[0][1]\n",
+        "\n",
+        "specificity = (TN/TN+FP)\n",
+        "\n",
+        "print ('Specificity = ', specificity)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Specificity =  10.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Vt5yCe496RoC",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "082a4a24-d607-44b9-d067-f69f77657a84"
+      },
+      "source": [
+        "senstivity = (TP/TP+FN)\n",
+        "\n",
+        "print ('Senstivity = ', senstivity)"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Senstivity =  4.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HrNkVFg8-_hj"
+      },
+      "source": [
+        "Feature Importance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qCRNG8uR-6mX",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9cbe2af3-6685-4e5e-9c62-6124190834ae"
+      },
+      "source": [
+        "# get importance\n",
+        "importance = model3.feature_importances_\n",
+        "\n",
+        "# summarize feature importance\n",
+        "for i,v in enumerate(importance):\n",
+        "    print('Feature: %0d, Score: %.5f' % (i,v))"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Feature: 0, Score: 0.07814\n",
+            "Feature: 1, Score: 0.04206\n",
+            "Feature: 2, Score: 0.16580\n",
+            "Feature: 3, Score: 0.07477\n",
+            "Feature: 4, Score: 0.07587\n",
+            "Feature: 5, Score: 0.00828\n",
+            "Feature: 6, Score: 0.02014\n",
+            "Feature: 7, Score: 0.12772\n",
+            "Feature: 8, Score: 0.06950\n",
+            "Feature: 9, Score: 0.09957\n",
+            "Feature: 10, Score: 0.04677\n",
+            "Feature: 11, Score: 0.11667\n",
+            "Feature: 12, Score: 0.07473\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tUtHmfsu_EOF",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 282
+        },
+        "outputId": "3ccc0576-c4dd-41ba-e83e-cac8040ced12"
+      },
+      "source": [
+        "index= data.columns[:-1]\n",
+        "importance = pd.Series(model3.feature_importances_, index=index)\n",
+        "importance.nlargest(13).plot(kind='barh', colormap='winter')"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c8d92dbd0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 393657c0ca00b43757d941064a52668cb76fefaa Mon Sep 17 00:00:00 2001
From: Kainat-Mahar <72600530+Kainat-Mahar@users.noreply.github.com>
Date: Fri, 22 Oct 2021 14:14:56 +0500
Subject: [PATCH 060/232] Add files via upload

---
 .../Bitcoin__Price_Prediction.ipynb           | 1027 +++++++++++++++++
 1 file changed, 1027 insertions(+)
 create mode 100644 Machine Learning/Bitcoin__Price_Prediction.ipynb

diff --git a/Machine Learning/Bitcoin__Price_Prediction.ipynb b/Machine Learning/Bitcoin__Price_Prediction.ipynb
new file mode 100644
index 0000000..10cc954
--- /dev/null
+++ b/Machine Learning/Bitcoin__Price_Prediction.ipynb	
@@ -0,0 +1,1027 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Bitcoin _Price_Prediction.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FmMO__YhieOb"
+      },
+      "source": [
+        "import json\n",
+        "import requests\n",
+        "import tensorflow as tf\n",
+        "from tensorflow import  keras\n",
+        "from tensorflow.keras import layers\n",
+        "#from tensorflow.contrib import lite\n",
+        "from keras.models import Sequential\n",
+        "from keras.layers import Activation, Dense, Dropout, LSTM\n",
+        "from keras.callbacks import EarlyStopping\n",
+        "import matplotlib.pyplot as plt\n",
+        "import plotly.offline as py\n",
+        "import plotly.graph_objs as go\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "from sklearn.metrics import mean_absolute_error\n",
+        "%matplotlib inline"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FBVKRHGfigX6"
+      },
+      "source": [
+        "endpoint = 'https://min-api.cryptocompare.com/data/histoday'\n",
+        "res = requests.get(endpoint + '?fsym=BTC&tsym=USD&limit=500')\n",
+        "data = pd.DataFrame(json.loads(res.content)['Data'])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IBY6ytPNik0L",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 227
+        },
+        "outputId": "9a29c462-99a5-4081-8849-f19db1a131f5"
+      },
+      "source": [
+        "data = data.set_index('time')\n",
+        "data.index = pd.to_datetime(data.index, unit='s')\n",
+        "data.head(5)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>high</th>\n",
+              "      <th>low</th>\n",
+              "      <th>open</th>\n",
+              "      <th>volumefrom</th>\n",
+              "      <th>volumeto</th>\n",
+              "      <th>close</th>\n",
+              "      <th>conversionType</th>\n",
+              "      <th>conversionSymbol</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>time</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2019-12-21</th>\n",
+              "      <td>7213.86</td>\n",
+              "      <td>7132.82</td>\n",
+              "      <td>7203.41</td>\n",
+              "      <td>12101.12</td>\n",
+              "      <td>8.686484e+07</td>\n",
+              "      <td>7159.47</td>\n",
+              "      <td>direct</td>\n",
+              "      <td></td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2019-12-22</th>\n",
+              "      <td>7527.40</td>\n",
+              "      <td>7143.17</td>\n",
+              "      <td>7159.47</td>\n",
+              "      <td>27325.26</td>\n",
+              "      <td>2.005866e+08</td>\n",
+              "      <td>7517.58</td>\n",
+              "      <td>direct</td>\n",
+              "      <td></td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2019-12-23</th>\n",
+              "      <td>7694.65</td>\n",
+              "      <td>7275.28</td>\n",
+              "      <td>7517.58</td>\n",
+              "      <td>44770.93</td>\n",
+              "      <td>3.364250e+08</td>\n",
+              "      <td>7326.60</td>\n",
+              "      <td>direct</td>\n",
+              "      <td></td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2019-12-24</th>\n",
+              "      <td>7429.42</td>\n",
+              "      <td>7175.50</td>\n",
+              "      <td>7326.60</td>\n",
+              "      <td>27770.75</td>\n",
+              "      <td>2.030134e+08</td>\n",
+              "      <td>7260.91</td>\n",
+              "      <td>direct</td>\n",
+              "      <td></td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2019-12-25</th>\n",
+              "      <td>7280.52</td>\n",
+              "      <td>7132.30</td>\n",
+              "      <td>7260.91</td>\n",
+              "      <td>15012.21</td>\n",
+              "      <td>1.085133e+08</td>\n",
+              "      <td>7202.72</td>\n",
+              "      <td>direct</td>\n",
+              "      <td></td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "               high      low     open  ...    close  conversionType  conversionSymbol\n",
+              "time                                   ...                                           \n",
+              "2019-12-21  7213.86  7132.82  7203.41  ...  7159.47          direct                  \n",
+              "2019-12-22  7527.40  7143.17  7159.47  ...  7517.58          direct                  \n",
+              "2019-12-23  7694.65  7275.28  7517.58  ...  7326.60          direct                  \n",
+              "2019-12-24  7429.42  7175.50  7326.60  ...  7260.91          direct                  \n",
+              "2019-12-25  7280.52  7132.30  7260.91  ...  7202.72          direct                  \n",
+              "\n",
+              "[5 rows x 8 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AnPPwQsXiliC",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "21546cfe-08d4-49b0-e5bd-f06af964fb6e"
+      },
+      "source": [
+        "target_col = data['close']\n",
+        "len(target_col)\n",
+        "target_col"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "time\n",
+              "2019-12-21     7159.47\n",
+              "2019-12-22     7517.58\n",
+              "2019-12-23     7326.60\n",
+              "2019-12-24     7260.91\n",
+              "2019-12-25     7202.72\n",
+              "                ...   \n",
+              "2021-04-30    57757.97\n",
+              "2021-05-01    57843.16\n",
+              "2021-05-02    56623.51\n",
+              "2021-05-03    57195.07\n",
+              "2021-05-04    56108.65\n",
+              "Name: close, Length: 501, dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LakZtj3riThB",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "78ff2ab8-b313-45fe-d53c-7e99658abe1f"
+      },
+      "source": [
+        "target_col.describe()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count      501.000000\n",
+              "mean     20289.575749\n",
+              "std      17327.126371\n",
+              "min       4916.780000\n",
+              "25%       9160.390000\n",
+              "50%      10929.400000\n",
+              "75%      28889.610000\n",
+              "max      63558.480000\n",
+              "Name: close, dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8mK8uToX7uKJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 392
+        },
+        "outputId": "51c951a7-cfbf-47ce-b2e2-c932eda94e5c"
+      },
+      "source": [
+        "plt.rcParams['figure.figsize'] = [10,6]\n",
+        "plt.plot(target_col)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7f518cd31e90>]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KPzobcXnqlyq"
+      },
+      "source": [
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "scaler=MinMaxScaler(feature_range=(0,1))\n",
+        "target_col=scaler.fit_transform(np.array(target_col).reshape(-1,1))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3ez8_5IvogUP"
+      },
+      "source": [
+        "##splitting dataset into train and test split\n",
+        "training_size=int(len(target_col)*0.80)\n",
+        "test_size=len(target_col)-training_size\n",
+        "train_data,test_data=target_col[0:training_size,:],target_col[training_size:len(target_col),:1]"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FMon8VWLr6jO"
+      },
+      "source": [
+        "# convert an array of values into a dataset matrix\n",
+        "def create_dataset(dataset, time_step=1):\n",
+        "\tdataX, dataY = [], []\n",
+        "\tfor i in range(len(dataset)-time_step-1):\n",
+        "\t\ta = dataset[i:(i+time_step), 0]   ###i=0, 0,1,2,3-----99   100 \n",
+        "\t\tdataX.append(a)\n",
+        "\t\tdataY.append(dataset[i + time_step, 0])\n",
+        "\treturn np.array(dataX), np.array(dataY)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "583kizUCsA7u"
+      },
+      "source": [
+        "# reshape into X=t,t+1,t+2,t+3 and Y=t+4\n",
+        "time_step=1\n",
+        "X_train, y_train = create_dataset(train_data, time_step)\n",
+        "X_test, ytest = create_dataset(test_data, time_step)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ePpTIUHOkAt3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9700ea2a-1782-4b5b-fc1a-e87a0a881a7e"
+      },
+      "source": [
+        "print(X_train.shape), print(y_train.shape)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(398, 1)\n",
+            "(398,)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(None, None)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CNaEHZOwkIj3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "33081e98-08c2-44e9-f78b-09776dd0fc6f"
+      },
+      "source": [
+        "print(X_test.shape), print(ytest.shape)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(99, 1)\n",
+            "(99,)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(None, None)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fJ-XaSzikOqB"
+      },
+      "source": [
+        "# reshape input to be [samples, time steps, features] which is required for LSTM\n",
+        "X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)\n",
+        "X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NVGFeeABkYCl"
+      },
+      "source": [
+        "model=keras.Sequential()\n",
+        "model.add(LSTM(500,return_sequences=True,input_shape=(X_train.shape[1], X_train.shape[2])))\n",
+        "model.add(LSTM(500,return_sequences=True))\n",
+        "model.add(LSTM(500,return_sequences=False))\n",
+        "model.add(Dropout(0.2))\n",
+        "model.add(Activation('sigmoid'))\n",
+        "model.add(Dense(1))\n",
+        "#opt = keras.optimizers.Adam(learning_rate=0.01)\n",
+        "#model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['mse'])\n",
+        "model.compile(loss='mean_squared_error',optimizer='adam', metrics=['mae'])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "xF8-LcpsqMt6",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5ef342e9-a93f-4a70-9316-72d3a0938303"
+      },
+      "source": [
+        "model.summary()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Model: \"sequential\"\n",
+            "_________________________________________________________________\n",
+            "Layer (type)                 Output Shape              Param #   \n",
+            "=================================================================\n",
+            "lstm (LSTM)                  (None, 1, 500)            1004000   \n",
+            "_________________________________________________________________\n",
+            "lstm_1 (LSTM)                (None, 1, 500)            2002000   \n",
+            "_________________________________________________________________\n",
+            "lstm_2 (LSTM)                (None, 500)               2002000   \n",
+            "_________________________________________________________________\n",
+            "dropout (Dropout)            (None, 500)               0         \n",
+            "_________________________________________________________________\n",
+            "activation (Activation)      (None, 500)               0         \n",
+            "_________________________________________________________________\n",
+            "dense (Dense)                (None, 1)                 501       \n",
+            "=================================================================\n",
+            "Total params: 5,008,501\n",
+            "Trainable params: 5,008,501\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "oLH_xT7qqdpT",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "446bc1f0-ddc3-4a52-c174-15ea63bf63a8"
+      },
+      "source": [
+        "history=model.fit(X_train,y_train,validation_data=(X_test,ytest),epochs=20,batch_size=32,verbose=1)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/20\n",
+            "13/13 [==============================] - 6s 162ms/step - loss: 0.0422 - mae: 0.1598 - val_loss: 0.3443 - val_mae: 0.5704\n",
+            "Epoch 2/20\n",
+            "13/13 [==============================] - 1s 42ms/step - loss: 0.0216 - mae: 0.1215 - val_loss: 0.4032 - val_mae: 0.6202\n",
+            "Epoch 3/20\n",
+            "13/13 [==============================] - 1s 42ms/step - loss: 0.0168 - mae: 0.0962 - val_loss: 0.3642 - val_mae: 0.5901\n",
+            "Epoch 4/20\n",
+            "13/13 [==============================] - 1s 42ms/step - loss: 0.0096 - mae: 0.0663 - val_loss: 0.1141 - val_mae: 0.3296\n",
+            "Epoch 5/20\n",
+            "13/13 [==============================] - 1s 41ms/step - loss: 0.0023 - mae: 0.0315 - val_loss: 0.0232 - val_mae: 0.1477\n",
+            "Epoch 6/20\n",
+            "13/13 [==============================] - 1s 43ms/step - loss: 0.0011 - mae: 0.0186 - val_loss: 0.0129 - val_mae: 0.1058\n",
+            "Epoch 7/20\n",
+            "13/13 [==============================] - 1s 44ms/step - loss: 3.7233e-04 - mae: 0.0123 - val_loss: 0.0049 - val_mae: 0.0604\n",
+            "Epoch 8/20\n",
+            "13/13 [==============================] - 1s 45ms/step - loss: 1.6180e-04 - mae: 0.0084 - val_loss: 0.0041 - val_mae: 0.0538\n",
+            "Epoch 9/20\n",
+            "13/13 [==============================] - 1s 43ms/step - loss: 1.6337e-04 - mae: 0.0091 - val_loss: 0.0064 - val_mae: 0.0707\n",
+            "Epoch 10/20\n",
+            "13/13 [==============================] - 1s 45ms/step - loss: 1.9074e-04 - mae: 0.0077 - val_loss: 0.0035 - val_mae: 0.0487\n",
+            "Epoch 11/20\n",
+            "13/13 [==============================] - 1s 41ms/step - loss: 1.1686e-04 - mae: 0.0067 - val_loss: 0.0044 - val_mae: 0.0561\n",
+            "Epoch 12/20\n",
+            "13/13 [==============================] - 1s 41ms/step - loss: 1.7812e-04 - mae: 0.0083 - val_loss: 0.0033 - val_mae: 0.0474\n",
+            "Epoch 13/20\n",
+            "13/13 [==============================] - 1s 42ms/step - loss: 1.6930e-04 - mae: 0.0077 - val_loss: 0.0055 - val_mae: 0.0643\n",
+            "Epoch 14/20\n",
+            "13/13 [==============================] - 1s 42ms/step - loss: 1.1692e-04 - mae: 0.0069 - val_loss: 0.0028 - val_mae: 0.0425\n",
+            "Epoch 15/20\n",
+            "13/13 [==============================] - 1s 42ms/step - loss: 1.2750e-04 - mae: 0.0074 - val_loss: 0.0043 - val_mae: 0.0553\n",
+            "Epoch 16/20\n",
+            "13/13 [==============================] - 1s 41ms/step - loss: 1.9787e-04 - mae: 0.0098 - val_loss: 0.0059 - val_mae: 0.0675\n",
+            "Epoch 17/20\n",
+            "13/13 [==============================] - 1s 41ms/step - loss: 2.1901e-04 - mae: 0.0115 - val_loss: 0.0030 - val_mae: 0.0441\n",
+            "Epoch 18/20\n",
+            "13/13 [==============================] - 1s 42ms/step - loss: 1.0347e-04 - mae: 0.0061 - val_loss: 0.0027 - val_mae: 0.0419\n",
+            "Epoch 19/20\n",
+            "13/13 [==============================] - 1s 41ms/step - loss: 1.2707e-04 - mae: 0.0068 - val_loss: 0.0032 - val_mae: 0.0466\n",
+            "Epoch 20/20\n",
+            "13/13 [==============================] - 1s 42ms/step - loss: 1.6478e-04 - mae: 0.0088 - val_loss: 0.0037 - val_mae: 0.0503\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2-RCPm8wjBUb",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bdbb3f64-e100-4436-d95d-169bc0e13d50"
+      },
+      "source": [
+        "history.history['loss']"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[0.03191959112882614,\n",
+              " 0.018243372440338135,\n",
+              " 0.014717623591423035,\n",
+              " 0.009163795970380306,\n",
+              " 0.0015256005572155118,\n",
+              " 0.0005994651583023369,\n",
+              " 0.0002838505315594375,\n",
+              " 0.00020431107259355485,\n",
+              " 0.00016604272241238505,\n",
+              " 0.00015689247811678797,\n",
+              " 0.00014251841639634222,\n",
+              " 0.00016275570669677109,\n",
+              " 0.000160191222676076,\n",
+              " 0.00011929321772186086,\n",
+              " 0.0001646284363232553,\n",
+              " 0.000203924355446361,\n",
+              " 0.00019159115618094802,\n",
+              " 0.0001174519638880156,\n",
+              " 0.00013711779320146888,\n",
+              " 0.00019837942090816796]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-Rkxen9OBHSw",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 616
+        },
+        "outputId": "f83471ee-7644-47b1-8fea-f271a09eb04a"
+      },
+      "source": [
+        "trace1 = go.Scatter(\n",
+        "    x = np.arange(0, len(history.history['loss']), 1),\n",
+        "    y = history.history['loss'],\n",
+        "    mode = 'lines',\n",
+        "    name = 'Train loss',\n",
+        "    line = dict(color=('rgb(255,0,0)'), width=2, dash='dash')\n",
+        ")\n",
+        "trace2 = go.Scatter(\n",
+        "    x = np.arange(0, len(history.history['val_loss']), 1),\n",
+        "    y = history.history['val_loss'],\n",
+        "    mode = 'lines',\n",
+        "    name = 'Test loss',\n",
+        "    line = dict(color=('rgb(1, 0, 255)'), width=2)\n",
+        ")\n",
+        "\n",
+        "data = [trace1, trace2]\n",
+        "layout = go.Layout(\n",
+        "    autosize=False,\n",
+        "    width=750,\n",
+        "    height=600,\n",
+        "\n",
+        "    title = 'Train and Test Loss during training',\n",
+        "    xaxis= go.layout.XAxis(linecolor = 'black',\n",
+        "                          linewidth = 1,\n",
+        "                          mirror = True,\n",
+        "                          title = \"Epoch number\",\n",
+        "                          color = 'black'\n",
+        "                          ),\n",
+        "\n",
+        "    yaxis= go.layout.YAxis(linecolor = 'red',\n",
+        "                          linewidth = 1,\n",
+        "                          mirror = True,\n",
+        "                          title = \"Loss\",\n",
+        "                           color = 'black'),\n",
+        "\n",
+        "    margin=go.layout.Margin(\n",
+        "        l=50,\n",
+        "        r=50,\n",
+        "        b=100,\n",
+        "        t=100,\n",
+        "        pad = 4\n",
+        "    )\n",
+        ")\n",
+        "\n",
+        "fig = go.Figure(data=data, layout=layout)\n",
+        "py.iplot(fig, filename='training_process')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
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+              "var x = new MutationObserver(function (mutations, observer) {{\n",
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+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })\n",
+              "                };\n",
+              "                \n",
+              "            </script>\n",
+              "        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wt88cq4-CE4C",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 616
+        },
+        "outputId": "bc8520e0-71b8-42f3-9d7e-f9f340911ba2"
+      },
+      "source": [
+        "trace1 = go.Scatter(\n",
+        "    x = np.arange(0, len(history.history['mae']), 1),\n",
+        "    y = history.history['mae'],\n",
+        "    mode = 'lines',\n",
+        "    name = 'Train Mean Absolute Error',\n",
+        "    line = dict(color=('rgb(0,0,139)'), width=2, dash='dash')\n",
+        ")\n",
+        "trace2 = go.Scatter(\n",
+        "    x = np.arange(0, len(history.history['val_mae']), 1),\n",
+        "    y = history.history['val_mae'],\n",
+        "    mode = 'lines',\n",
+        "    name = 'Test Mean Absolute Error',\n",
+        "    line = dict(color=('rgb(1, 0, 0)'), width=2)\n",
+        ")\n",
+        "\n",
+        "data = [trace1, trace2]\n",
+        "layout = go.Layout(\n",
+        "    autosize=False,\n",
+        "    width=750,\n",
+        "    height=600,\n",
+        "\n",
+        "    title = 'Train and Test Mean Squared Error during training',\n",
+        "    xaxis= go.layout.XAxis(linecolor = 'black',\n",
+        "                          linewidth = 1,\n",
+        "                          mirror = True,\n",
+        "                          title = \"Epoch number\",\n",
+        "                          color = 'black'\n",
+        "                          ),\n",
+        "\n",
+        "    yaxis= go.layout.YAxis(linecolor = 'red',\n",
+        "                          linewidth = 1,\n",
+        "                          mirror = True,\n",
+        "                          title = \"Mean Absolute Error\",\n",
+        "                           color = 'black'),\n",
+        "\n",
+        "    margin=go.layout.Margin(\n",
+        "        l=50,\n",
+        "        r=50,\n",
+        "        b=100,\n",
+        "        t=100,\n",
+        "        pad = 4\n",
+        "    )\n",
+        ")\n",
+        "\n",
+        "fig = go.Figure(data=data, layout=layout)\n",
+        "py.iplot(fig, filename='training_process')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>\n",
+              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
+              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
+              "            <div id=\"6111f917-251c-4833-a48a-d2fe25d0ffff\" class=\"plotly-graph-div\" style=\"height:600px; width:750px;\"></div>\n",
+              "            <script type=\"text/javascript\">\n",
+              "                \n",
+              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
+              "                    \n",
+              "                if (document.getElementById(\"6111f917-251c-4833-a48a-d2fe25d0ffff\")) {\n",
+              "                    Plotly.newPlot(\n",
+              "                        '6111f917-251c-4833-a48a-d2fe25d0ffff',\n",
+              "                        [{\"line\": {\"color\": \"rgb(0,0,139)\", \"dash\": \"dash\", \"width\": 2}, \"mode\": \"lines\", \"name\": \"Train Mean Absolute Error\", \"type\": \"scatter\", \"x\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], \"y\": [0.13227638602256775, 0.09827647358179092, 0.08509460836648941, 0.06485840678215027, 0.02389504760503769, 0.014748598448932171, 0.010887573473155499, 0.008769395761191845, 0.008869457058608532, 0.007498753722757101, 0.00709223048761487, 0.00829450972378254, 0.007124415133148432, 0.006769220344722271, 0.0081796208396554, 0.010284418240189552, 0.010304966941475868, 0.00639445660635829, 0.006888981442898512, 0.010113021358847618]}, {\"line\": {\"color\": \"rgb(1, 0, 0)\", \"width\": 2}, \"mode\": \"lines\", \"name\": \"Test Mean Absolute Error\", \"type\": \"scatter\", \"x\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], \"y\": [0.5704196691513062, 0.6201988458633423, 0.590086042881012, 0.32956239581108093, 0.14767205715179443, 0.10583365708589554, 0.06040019169449806, 0.053754955530166626, 0.07072297483682632, 0.04867550730705261, 0.05613739416003227, 0.04735608398914337, 0.06426912546157837, 0.042503803968429565, 0.055320776998996735, 0.06749662756919861, 0.044106725603342056, 0.04187443107366562, 0.04664134979248047, 0.05027846246957779]}],\n",
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\"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Train and Test Mean Squared Error during training\"}, \"width\": 750, \"xaxis\": {\"color\": \"black\", \"linecolor\": \"black\", \"linewidth\": 1, \"mirror\": true, \"title\": {\"text\": \"Epoch number\"}}, \"yaxis\": {\"color\": \"black\", \"linecolor\": \"red\", \"linewidth\": 1, \"mirror\": true, \"title\": {\"text\": \"Mean Absolute Error\"}}},\n",
+              "                        {\"responsive\": true}\n",
+              "                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('6111f917-251c-4833-a48a-d2fe25d0ffff');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })\n",
+              "                };\n",
+              "                \n",
+              "            </script>\n",
+              "        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jgbBKA6NClxf"
+      },
+      "source": [
+        "# add one additional data point to align shapes of the predictions and true labels\n",
+        "#X_test = np.append(X_test, scaler.transform(data.iloc[-1][0]))\n",
+        "#X_test = np.reshape(X_test, (len(X_test), 1, 1))\n",
+        "\n",
+        "# get predictions and then make some transformations to be able to calculate RMSE properly in USD\n",
+        "prediction = model.predict(X_test)\n",
+        "prediction_inverse = scaler.inverse_transform(prediction.reshape(-1, 1))\n",
+        "Y_test_inverse = scaler.inverse_transform(ytest.reshape(-1, 1))\n",
+        "prediction2_inverse = np.array(prediction_inverse[:,0][1:])\n",
+        "Y_test2_inverse = np.array(Y_test_inverse[:,0])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "m8SXDEzbDPzM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f8613de7-48ba-4dc6-d1b1-618cca8b5402"
+      },
+      "source": [
+        "trace1 = go.Scatter(\n",
+        "    x = np.arange(0, len(prediction2_inverse), 1),\n",
+        "    y = prediction2_inverse,\n",
+        "    mode = 'lines',\n",
+        "    name = 'Predicted Labels',\n",
+        "    line = dict(color=('rgb(0,0,139)'), width=2)\n",
+        ")\n",
+        "trace2 = go.Scatter(\n",
+        "    x = np.arange(0, len(Y_test2_inverse), 1),\n",
+        "    y = Y_test2_inverse,\n",
+        "    mode = 'lines',\n",
+        "    name = 'True labels',\n",
+        "    line = dict(color=('rgb(255, 0, 0)'), width=2)\n",
+        "\n",
+        ")\n",
+        "data = [trace1, trace2]\n",
+        "\n",
+        "layout = go.Layout(\n",
+        "    autosize=False,\n",
+        "    width=750,\n",
+        "    height=600,\n",
+        "\n",
+        "    title = 'Prediction Graph',\n",
+        "    xaxis= go.layout.XAxis(linecolor = 'black',\n",
+        "                          linewidth = 1,\n",
+        "                          mirror = True,\n",
+        "                          title = \"Number of days\",\n",
+        "                          color = 'black'\n",
+        "                          ),                          \n",
+        "\n",
+        "    yaxis= go.layout.YAxis(linecolor = 'red',\n",
+        "                          linewidth = 1,\n",
+        "                          mirror = True,\n",
+        "                          title = \"Price in USD\",\n",
+        "                           color = 'black'),\n",
+        "\n",
+        "    margin=go.layout.Margin(\n",
+        "        l=50,\n",
+        "        r=50,\n",
+        "        b=100,\n",
+        "        t=100,\n",
+        "        pad = 4\n",
+        "    )\n",
+        ")\n",
+        "\n",
+        "fig = go.Figure(data=data, layout=layout)\n",
+        "py.iplot(fig, filename='results_demonstrating0')\n",
+        "plt.savefig('demonstrating0.png', dpi=300, transparent=False, bbox_inches='tight')\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>\n",
+              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
+              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
+              "            <div id=\"a79c7a85-6d21-421d-a360-a708d2b9e71f\" class=\"plotly-graph-div\" style=\"height:600px; width:750px;\"></div>\n",
+              "            <script type=\"text/javascript\">\n",
+              "                \n",
+              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
+              "                    \n",
+              "                if (document.getElementById(\"a79c7a85-6d21-421d-a360-a708d2b9e71f\")) {\n",
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+              "                        {\"responsive\": true}\n",
+              "                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('a79c7a85-6d21-421d-a360-a708d2b9e71f');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })\n",
+              "                };\n",
+              "                \n",
+              "            </script>\n",
+              "        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x432 with 0 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 76a33e2480fd28871965ac536c2f44c15d5bdf77 Mon Sep 17 00:00:00 2001
From: Kainat-Mahar <72600530+Kainat-Mahar@users.noreply.github.com>
Date: Fri, 22 Oct 2021 14:23:00 +0500
Subject: [PATCH 061/232] Delete
 Classification_using_Random_Forest_Algorithm.ipynb

---
 ...cation_using_Random_Forest_Algorithm.ipynb | 1355 -----------------
 1 file changed, 1355 deletions(-)
 delete mode 100644 Data Science/Classification_using_Random_Forest_Algorithm.ipynb

diff --git a/Data Science/Classification_using_Random_Forest_Algorithm.ipynb b/Data Science/Classification_using_Random_Forest_Algorithm.ipynb
deleted file mode 100644
index ef5e38c..0000000
--- a/Data Science/Classification_using_Random_Forest_Algorithm.ipynb	
+++ /dev/null
@@ -1,1355 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "Classification using Random Forest Algorithm.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "HbbGwEA47kDj"
-      },
-      "source": [
-        "Importing Libraries"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "roYk-Q3s6yVd"
-      },
-      "source": [
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import matplotlib as plt\n",
-        "import seaborn as sns\n",
-        "import matplotlib.pyplot as plt"
-      ],
-      "execution_count": 1,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "7v8I08N58jcA"
-      },
-      "source": [
-        "Data Wrangling"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Ttq0PCrZ8i_d",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 202
-        },
-        "outputId": "d2a6d459-e354-475f-d2c5-ef08982f82d0"
-      },
-      "source": [
-        "data = pd.read_csv('dataset.csv')\n",
-        "\n",
-        "data.head(5)"
-      ],
-      "execution_count": 2,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>age</th>\n",
-              "      <th>sex</th>\n",
-              "      <th>cp</th>\n",
-              "      <th>trestbps</th>\n",
-              "      <th>chol</th>\n",
-              "      <th>fbs</th>\n",
-              "      <th>restecg</th>\n",
-              "      <th>thalach</th>\n",
-              "      <th>exang</th>\n",
-              "      <th>oldpeak</th>\n",
-              "      <th>slope</th>\n",
-              "      <th>ca</th>\n",
-              "      <th>thal</th>\n",
-              "      <th>target</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>63</td>\n",
-              "      <td>1</td>\n",
-              "      <td>3</td>\n",
-              "      <td>145</td>\n",
-              "      <td>233</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>150</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2.3</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>37</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>130</td>\n",
-              "      <td>250</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>187</td>\n",
-              "      <td>0</td>\n",
-              "      <td>3.5</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>41</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>130</td>\n",
-              "      <td>204</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>172</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1.4</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>56</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>120</td>\n",
-              "      <td>236</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>178</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0.8</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>57</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>120</td>\n",
-              "      <td>354</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>163</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0.6</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
-              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
-              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
-              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
-              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
-              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
-              "\n",
-              "[5 rows x 14 columns]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 2
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "8PJwi9rc8tp-",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "4e8f191b-6599-496e-b97b-8fcbedbc7b22"
-      },
-      "source": [
-        "print(\"(Rows, columns): \" + str(data.shape))\n",
-        "data.columns "
-      ],
-      "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "(Rows, columns): (303, 14)\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
-              "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
-              "      dtype='object')"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 3
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "PxY79ivL80gB",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "73b9d73d-e643-4602-ac52-c8df5d5a8413"
-      },
-      "source": [
-        "data.nunique(axis=0)# returns the number of unique values for each variable."
-      ],
-      "execution_count": 4,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "age          41\n",
-              "sex           2\n",
-              "cp            4\n",
-              "trestbps     49\n",
-              "chol        152\n",
-              "fbs           2\n",
-              "restecg       3\n",
-              "thalach      91\n",
-              "exang         2\n",
-              "oldpeak      40\n",
-              "slope         3\n",
-              "ca            5\n",
-              "thal          4\n",
-              "target        2\n",
-              "dtype: int64"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 4
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "wn63NFgG88dd",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 477
-        },
-        "outputId": "83fe2164-71d8-438a-f38f-6076eb0512c6"
-      },
-      "source": [
-        "#summarizes the count, mean, standard deviation, min, and max for numeric variables.\n",
-        "data.describe().transpose()"
-      ],
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>count</th>\n",
-              "      <th>mean</th>\n",
-              "      <th>std</th>\n",
-              "      <th>min</th>\n",
-              "      <th>25%</th>\n",
-              "      <th>50%</th>\n",
-              "      <th>75%</th>\n",
-              "      <th>max</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>age</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>54.366337</td>\n",
-              "      <td>9.082101</td>\n",
-              "      <td>29.0</td>\n",
-              "      <td>47.5</td>\n",
-              "      <td>55.0</td>\n",
-              "      <td>61.0</td>\n",
-              "      <td>77.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>sex</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>0.683168</td>\n",
-              "      <td>0.466011</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cp</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>0.966997</td>\n",
-              "      <td>1.032052</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>3.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>trestbps</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>131.623762</td>\n",
-              "      <td>17.538143</td>\n",
-              "      <td>94.0</td>\n",
-              "      <td>120.0</td>\n",
-              "      <td>130.0</td>\n",
-              "      <td>140.0</td>\n",
-              "      <td>200.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>chol</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>246.264026</td>\n",
-              "      <td>51.830751</td>\n",
-              "      <td>126.0</td>\n",
-              "      <td>211.0</td>\n",
-              "      <td>240.0</td>\n",
-              "      <td>274.5</td>\n",
-              "      <td>564.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>fbs</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>0.148515</td>\n",
-              "      <td>0.356198</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>restecg</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>0.528053</td>\n",
-              "      <td>0.525860</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>thalach</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>149.646865</td>\n",
-              "      <td>22.905161</td>\n",
-              "      <td>71.0</td>\n",
-              "      <td>133.5</td>\n",
-              "      <td>153.0</td>\n",
-              "      <td>166.0</td>\n",
-              "      <td>202.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>exang</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>0.326733</td>\n",
-              "      <td>0.469794</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>oldpeak</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>1.039604</td>\n",
-              "      <td>1.161075</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.8</td>\n",
-              "      <td>1.6</td>\n",
-              "      <td>6.2</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>slope</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>1.399340</td>\n",
-              "      <td>0.616226</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>2.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>ca</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>0.729373</td>\n",
-              "      <td>1.022606</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>4.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>thal</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>2.313531</td>\n",
-              "      <td>0.612277</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>3.0</td>\n",
-              "      <td>3.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>target</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>0.544554</td>\n",
-              "      <td>0.498835</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "          count        mean        std    min    25%    50%    75%    max\n",
-              "age       303.0   54.366337   9.082101   29.0   47.5   55.0   61.0   77.0\n",
-              "sex       303.0    0.683168   0.466011    0.0    0.0    1.0    1.0    1.0\n",
-              "cp        303.0    0.966997   1.032052    0.0    0.0    1.0    2.0    3.0\n",
-              "trestbps  303.0  131.623762  17.538143   94.0  120.0  130.0  140.0  200.0\n",
-              "chol      303.0  246.264026  51.830751  126.0  211.0  240.0  274.5  564.0\n",
-              "fbs       303.0    0.148515   0.356198    0.0    0.0    0.0    0.0    1.0\n",
-              "restecg   303.0    0.528053   0.525860    0.0    0.0    1.0    1.0    2.0\n",
-              "thalach   303.0  149.646865  22.905161   71.0  133.5  153.0  166.0  202.0\n",
-              "exang     303.0    0.326733   0.469794    0.0    0.0    0.0    1.0    1.0\n",
-              "oldpeak   303.0    1.039604   1.161075    0.0    0.0    0.8    1.6    6.2\n",
-              "slope     303.0    1.399340   0.616226    0.0    1.0    1.0    2.0    2.0\n",
-              "ca        303.0    0.729373   1.022606    0.0    0.0    0.0    1.0    4.0\n",
-              "thal      303.0    2.313531   0.612277    0.0    2.0    2.0    3.0    3.0\n",
-              "target    303.0    0.544554   0.498835    0.0    0.0    1.0    1.0    1.0"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "a6ushlSP9EJP",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "366a429a-9d57-4ea8-bc8d-b51b0baf45fc"
-      },
-      "source": [
-        "# Display the Missing Values\n",
-        "\n",
-        "print(data.isna().sum())"
-      ],
-      "execution_count": 6,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "age         0\n",
-            "sex         0\n",
-            "cp          0\n",
-            "trestbps    0\n",
-            "chol        0\n",
-            "fbs         0\n",
-            "restecg     0\n",
-            "thalach     0\n",
-            "exang       0\n",
-            "oldpeak     0\n",
-            "slope       0\n",
-            "ca          0\n",
-            "thal        0\n",
-            "target      0\n",
-            "dtype: int64\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "IXOMCsIl9JpJ",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 612
-        },
-        "outputId": "7b01ed95-db2a-435c-f6e5-c8423976082c"
-      },
-      "source": [
-        "# calculate correlation matrix\n",
-        "\n",
-        "corr = data.corr()\n",
-        "plt.subplots(figsize=(15,10))\n",
-        "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, annot=True, cmap=sns.diverging_palette(220, 20, as_cmap=True))\n",
-        "sns.heatmap(corr, xticklabels=corr.columns,\n",
-        "            yticklabels=corr.columns, \n",
-        "            annot=True,\n",
-        "            cmap=sns.diverging_palette(220, 20, as_cmap=True))"
-      ],
-      "execution_count": 7,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c953e9d10>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 7
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 1080x720 with 3 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qqZ2RHNI9gFK"
-      },
-      "source": [
-        "Filtering data by positive & negative Heart Disease patient"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "n9lROx7X9T2W",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 314
-        },
-        "outputId": "ca94464a-e45e-4429-d316-efebe625f5d0"
-      },
-      "source": [
-        "# Filtering data by POSITIVE Heart Disease patient\n",
-        "pos_data = data[data['target']==1]\n",
-        "pos_data.describe()"
-      ],
-      "execution_count": 8,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>age</th>\n",
-              "      <th>sex</th>\n",
-              "      <th>cp</th>\n",
-              "      <th>trestbps</th>\n",
-              "      <th>chol</th>\n",
-              "      <th>fbs</th>\n",
-              "      <th>restecg</th>\n",
-              "      <th>thalach</th>\n",
-              "      <th>exang</th>\n",
-              "      <th>oldpeak</th>\n",
-              "      <th>slope</th>\n",
-              "      <th>ca</th>\n",
-              "      <th>thal</th>\n",
-              "      <th>target</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>52.496970</td>\n",
-              "      <td>0.563636</td>\n",
-              "      <td>1.375758</td>\n",
-              "      <td>129.303030</td>\n",
-              "      <td>242.230303</td>\n",
-              "      <td>0.139394</td>\n",
-              "      <td>0.593939</td>\n",
-              "      <td>158.466667</td>\n",
-              "      <td>0.139394</td>\n",
-              "      <td>0.583030</td>\n",
-              "      <td>1.593939</td>\n",
-              "      <td>0.363636</td>\n",
-              "      <td>2.121212</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>9.550651</td>\n",
-              "      <td>0.497444</td>\n",
-              "      <td>0.952222</td>\n",
-              "      <td>16.169613</td>\n",
-              "      <td>53.552872</td>\n",
-              "      <td>0.347412</td>\n",
-              "      <td>0.504818</td>\n",
-              "      <td>19.174276</td>\n",
-              "      <td>0.347412</td>\n",
-              "      <td>0.780683</td>\n",
-              "      <td>0.593635</td>\n",
-              "      <td>0.848894</td>\n",
-              "      <td>0.465752</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>min</th>\n",
-              "      <td>29.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>94.000000</td>\n",
-              "      <td>126.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>96.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>25%</th>\n",
-              "      <td>44.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>120.000000</td>\n",
-              "      <td>208.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>149.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>50%</th>\n",
-              "      <td>52.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>130.000000</td>\n",
-              "      <td>234.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>161.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.200000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>75%</th>\n",
-              "      <td>59.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>140.000000</td>\n",
-              "      <td>267.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>172.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>max</th>\n",
-              "      <td>76.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>180.000000</td>\n",
-              "      <td>564.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>202.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>4.200000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>4.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "              age         sex          cp  ...          ca        thal  target\n",
-              "count  165.000000  165.000000  165.000000  ...  165.000000  165.000000   165.0\n",
-              "mean    52.496970    0.563636    1.375758  ...    0.363636    2.121212     1.0\n",
-              "std      9.550651    0.497444    0.952222  ...    0.848894    0.465752     0.0\n",
-              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000     1.0\n",
-              "25%     44.000000    0.000000    1.000000  ...    0.000000    2.000000     1.0\n",
-              "50%     52.000000    1.000000    2.000000  ...    0.000000    2.000000     1.0\n",
-              "75%     59.000000    1.000000    2.000000  ...    0.000000    2.000000     1.0\n",
-              "max     76.000000    1.000000    3.000000  ...    4.000000    3.000000     1.0\n",
-              "\n",
-              "[8 rows x 14 columns]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 8
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "iNdi8cwN9wmU",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 314
-        },
-        "outputId": "1e819b7f-f8cc-4418-f4f7-d5c8c010c77e"
-      },
-      "source": [
-        "# Filtering data by NEGATIVE Heart Disease patient\n",
-        "pos_data = data[data['target']==0]\n",
-        "pos_data.describe()"
-      ],
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>age</th>\n",
-              "      <th>sex</th>\n",
-              "      <th>cp</th>\n",
-              "      <th>trestbps</th>\n",
-              "      <th>chol</th>\n",
-              "      <th>fbs</th>\n",
-              "      <th>restecg</th>\n",
-              "      <th>thalach</th>\n",
-              "      <th>exang</th>\n",
-              "      <th>oldpeak</th>\n",
-              "      <th>slope</th>\n",
-              "      <th>ca</th>\n",
-              "      <th>thal</th>\n",
-              "      <th>target</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>56.601449</td>\n",
-              "      <td>0.826087</td>\n",
-              "      <td>0.478261</td>\n",
-              "      <td>134.398551</td>\n",
-              "      <td>251.086957</td>\n",
-              "      <td>0.159420</td>\n",
-              "      <td>0.449275</td>\n",
-              "      <td>139.101449</td>\n",
-              "      <td>0.550725</td>\n",
-              "      <td>1.585507</td>\n",
-              "      <td>1.166667</td>\n",
-              "      <td>1.166667</td>\n",
-              "      <td>2.543478</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>7.962082</td>\n",
-              "      <td>0.380416</td>\n",
-              "      <td>0.905920</td>\n",
-              "      <td>18.729944</td>\n",
-              "      <td>49.454614</td>\n",
-              "      <td>0.367401</td>\n",
-              "      <td>0.541321</td>\n",
-              "      <td>22.598782</td>\n",
-              "      <td>0.499232</td>\n",
-              "      <td>1.300340</td>\n",
-              "      <td>0.561324</td>\n",
-              "      <td>1.043460</td>\n",
-              "      <td>0.684762</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>min</th>\n",
-              "      <td>35.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>100.000000</td>\n",
-              "      <td>131.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>71.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>25%</th>\n",
-              "      <td>52.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>120.000000</td>\n",
-              "      <td>217.250000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>125.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.600000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>50%</th>\n",
-              "      <td>58.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>130.000000</td>\n",
-              "      <td>249.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>142.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>1.400000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>75%</th>\n",
-              "      <td>62.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>144.750000</td>\n",
-              "      <td>283.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>156.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>2.500000</td>\n",
-              "      <td>1.750000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>max</th>\n",
-              "      <td>77.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>200.000000</td>\n",
-              "      <td>409.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>195.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>6.200000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>4.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "              age         sex          cp  ...          ca        thal  target\n",
-              "count  138.000000  138.000000  138.000000  ...  138.000000  138.000000   138.0\n",
-              "mean    56.601449    0.826087    0.478261  ...    1.166667    2.543478     0.0\n",
-              "std      7.962082    0.380416    0.905920  ...    1.043460    0.684762     0.0\n",
-              "min     35.000000    0.000000    0.000000  ...    0.000000    0.000000     0.0\n",
-              "25%     52.000000    1.000000    0.000000  ...    0.000000    2.000000     0.0\n",
-              "50%     58.000000    1.000000    0.000000  ...    1.000000    3.000000     0.0\n",
-              "75%     62.000000    1.000000    0.000000  ...    2.000000    3.000000     0.0\n",
-              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000     0.0\n",
-              "\n",
-              "[8 rows x 14 columns]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 9
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DbzJPYQV9lPu"
-      },
-      "source": [
-        "Assign the 13 features to X, & the last column to our classification predictor, y"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "FjMSVs94960R"
-      },
-      "source": [
-        "X = data.iloc[:, :-1].values\n",
-        "y = data.iloc[:, -1].values"
-      ],
-      "execution_count": 10,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "kYwABWTb-AJx"
-      },
-      "source": [
-        "Split: the data set into the Training set and Test set"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "DlgSSiYE99tk"
-      },
-      "source": [
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(X,y,test_size = 0.2, random_state = 1)"
-      ],
-      "execution_count": 11,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "MuoZklrK-FJY"
-      },
-      "source": [
-        "Normalize: Standardizing the data a mean of 0 and a standard deviation of 1."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "rGfWqZir-LLs"
-      },
-      "source": [
-        "from sklearn.preprocessing import StandardScaler\n",
-        "sc = StandardScaler()\n",
-        "x_train = sc.fit_transform(x_train)\n",
-        "x_test = sc.transform(x_test)"
-      ],
-      "execution_count": 12,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "CwBqTSrI-WVv"
-      },
-      "source": [
-        "Modeling /Training"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "xk0Uem6P-qgq"
-      },
-      "source": [
-        "Model: Random Forest\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "JaSaSKDT-qE-",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "bbcc9de5-0a3e-4a70-9eb9-3530cc7803e0"
-      },
-      "source": [
-        "from sklearn.metrics import classification_report \n",
-        "from sklearn.ensemble import RandomForestClassifier\n",
-        "\n",
-        "model3 = RandomForestClassifier(random_state=1)# get instance of model\n",
-        "model3.fit(x_train, y_train) # Train/Fit model \n",
-        "\n",
-        "y_pred3 = model3.predict(x_test) # get y predictions\n",
-        "print(classification_report(y_test, y_pred3)) # output accuracy"
-      ],
-      "execution_count": 13,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "              precision    recall  f1-score   support\n",
-            "\n",
-            "           0       0.88      0.70      0.78        30\n",
-            "           1       0.76      0.90      0.82        31\n",
-            "\n",
-            "    accuracy                           0.80        61\n",
-            "   macro avg       0.82      0.80      0.80        61\n",
-            "weighted avg       0.81      0.80      0.80        61\n",
-            "\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "NYkRl2Et-1vo"
-      },
-      "source": [
-        "Making the Confusion Matrix"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "qH0hRD8j-3m9",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "f8f38c70-ce4c-4824-9ba3-13d9e6b06f5b"
-      },
-      "source": [
-        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
-        "cm = confusion_matrix(y_test, y_pred3)\n",
-        "print(cm)\n",
-        "accuracy_score(y_test, y_pred3)"
-      ],
-      "execution_count": 14,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "[[21  9]\n",
-            " [ 3 28]]\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.8032786885245902"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 14
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "fu7KQOnjzY6g",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "87984799-bb19-4075-de5c-d83033940f4c"
-      },
-      "source": [
-        "TN = cm[0][0]\n",
-        "FN = cm[1][0]\n",
-        "TP = cm[1][1]\n",
-        "FP = cm[0][1]\n",
-        "\n",
-        "specificity = (TN/TN+FP)\n",
-        "\n",
-        "print ('Specificity = ', specificity)"
-      ],
-      "execution_count": 15,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Specificity =  10.0\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Vt5yCe496RoC",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "082a4a24-d607-44b9-d067-f69f77657a84"
-      },
-      "source": [
-        "senstivity = (TP/TP+FN)\n",
-        "\n",
-        "print ('Senstivity = ', senstivity)"
-      ],
-      "execution_count": 16,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Senstivity =  4.0\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "HrNkVFg8-_hj"
-      },
-      "source": [
-        "Feature Importance"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "qCRNG8uR-6mX",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "9cbe2af3-6685-4e5e-9c62-6124190834ae"
-      },
-      "source": [
-        "# get importance\n",
-        "importance = model3.feature_importances_\n",
-        "\n",
-        "# summarize feature importance\n",
-        "for i,v in enumerate(importance):\n",
-        "    print('Feature: %0d, Score: %.5f' % (i,v))"
-      ],
-      "execution_count": 17,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Feature: 0, Score: 0.07814\n",
-            "Feature: 1, Score: 0.04206\n",
-            "Feature: 2, Score: 0.16580\n",
-            "Feature: 3, Score: 0.07477\n",
-            "Feature: 4, Score: 0.07587\n",
-            "Feature: 5, Score: 0.00828\n",
-            "Feature: 6, Score: 0.02014\n",
-            "Feature: 7, Score: 0.12772\n",
-            "Feature: 8, Score: 0.06950\n",
-            "Feature: 9, Score: 0.09957\n",
-            "Feature: 10, Score: 0.04677\n",
-            "Feature: 11, Score: 0.11667\n",
-            "Feature: 12, Score: 0.07473\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "tUtHmfsu_EOF",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 282
-        },
-        "outputId": "3ccc0576-c4dd-41ba-e83e-cac8040ced12"
-      },
-      "source": [
-        "index= data.columns[:-1]\n",
-        "importance = pd.Series(model3.feature_importances_, index=index)\n",
-        "importance.nlargest(13).plot(kind='barh', colormap='winter')"
-      ],
-      "execution_count": 18,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c8d92dbd0>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 18
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file

From 58851aae75c0731d1474f96ec30aabffd16529f4 Mon Sep 17 00:00:00 2001
From: Kainat-Mahar <72600530+Kainat-Mahar@users.noreply.github.com>
Date: Fri, 22 Oct 2021 14:23:58 +0500
Subject: [PATCH 062/232] Add files via upload

---
 ...cation_using_Random_Forest_Algorithm.ipynb | 1355 +++++++++++++++++
 1 file changed, 1355 insertions(+)
 create mode 100644 Data Science/Classification_using_Random_Forest_Algorithm.ipynb

diff --git a/Data Science/Classification_using_Random_Forest_Algorithm.ipynb b/Data Science/Classification_using_Random_Forest_Algorithm.ipynb
new file mode 100644
index 0000000..ef5e38c
--- /dev/null
+++ b/Data Science/Classification_using_Random_Forest_Algorithm.ipynb	
@@ -0,0 +1,1355 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Classification using Random Forest Algorithm.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HbbGwEA47kDj"
+      },
+      "source": [
+        "Importing Libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "roYk-Q3s6yVd"
+      },
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib as plt\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7v8I08N58jcA"
+      },
+      "source": [
+        "Data Wrangling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ttq0PCrZ8i_d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 202
+        },
+        "outputId": "d2a6d459-e354-475f-d2c5-ef08982f82d0"
+      },
+      "source": [
+        "data = pd.read_csv('dataset.csv')\n",
+        "\n",
+        "data.head(5)"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>cp</th>\n",
+              "      <th>trestbps</th>\n",
+              "      <th>chol</th>\n",
+              "      <th>fbs</th>\n",
+              "      <th>restecg</th>\n",
+              "      <th>thalach</th>\n",
+              "      <th>exang</th>\n",
+              "      <th>oldpeak</th>\n",
+              "      <th>slope</th>\n",
+              "      <th>ca</th>\n",
+              "      <th>thal</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>63</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>145</td>\n",
+              "      <td>233</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>150</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>37</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>130</td>\n",
+              "      <td>250</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>187</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>41</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>130</td>\n",
+              "      <td>204</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>172</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>56</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>120</td>\n",
+              "      <td>236</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>178</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>57</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>120</td>\n",
+              "      <td>354</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>163</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
+              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
+              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
+              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
+              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
+              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
+              "\n",
+              "[5 rows x 14 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8PJwi9rc8tp-",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4e8f191b-6599-496e-b97b-8fcbedbc7b22"
+      },
+      "source": [
+        "print(\"(Rows, columns): \" + str(data.shape))\n",
+        "data.columns "
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(Rows, columns): (303, 14)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
+              "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PxY79ivL80gB",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "73b9d73d-e643-4602-ac52-c8df5d5a8413"
+      },
+      "source": [
+        "data.nunique(axis=0)# returns the number of unique values for each variable."
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "age          41\n",
+              "sex           2\n",
+              "cp            4\n",
+              "trestbps     49\n",
+              "chol        152\n",
+              "fbs           2\n",
+              "restecg       3\n",
+              "thalach      91\n",
+              "exang         2\n",
+              "oldpeak      40\n",
+              "slope         3\n",
+              "ca            5\n",
+              "thal          4\n",
+              "target        2\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wn63NFgG88dd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 477
+        },
+        "outputId": "83fe2164-71d8-438a-f38f-6076eb0512c6"
+      },
+      "source": [
+        "#summarizes the count, mean, standard deviation, min, and max for numeric variables.\n",
+        "data.describe().transpose()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>min</th>\n",
+              "      <th>25%</th>\n",
+              "      <th>50%</th>\n",
+              "      <th>75%</th>\n",
+              "      <th>max</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>age</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>54.366337</td>\n",
+              "      <td>9.082101</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>47.5</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>61.0</td>\n",
+              "      <td>77.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sex</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.683168</td>\n",
+              "      <td>0.466011</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cp</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.966997</td>\n",
+              "      <td>1.032052</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>trestbps</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>131.623762</td>\n",
+              "      <td>17.538143</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>130.0</td>\n",
+              "      <td>140.0</td>\n",
+              "      <td>200.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>chol</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>246.264026</td>\n",
+              "      <td>51.830751</td>\n",
+              "      <td>126.0</td>\n",
+              "      <td>211.0</td>\n",
+              "      <td>240.0</td>\n",
+              "      <td>274.5</td>\n",
+              "      <td>564.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>fbs</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.148515</td>\n",
+              "      <td>0.356198</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>restecg</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.528053</td>\n",
+              "      <td>0.525860</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>thalach</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>149.646865</td>\n",
+              "      <td>22.905161</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>133.5</td>\n",
+              "      <td>153.0</td>\n",
+              "      <td>166.0</td>\n",
+              "      <td>202.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>exang</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.326733</td>\n",
+              "      <td>0.469794</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>oldpeak</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>1.039604</td>\n",
+              "      <td>1.161075</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>6.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>slope</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>1.399340</td>\n",
+              "      <td>0.616226</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ca</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.729373</td>\n",
+              "      <td>1.022606</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>thal</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>2.313531</td>\n",
+              "      <td>0.612277</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.544554</td>\n",
+              "      <td>0.498835</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          count        mean        std    min    25%    50%    75%    max\n",
+              "age       303.0   54.366337   9.082101   29.0   47.5   55.0   61.0   77.0\n",
+              "sex       303.0    0.683168   0.466011    0.0    0.0    1.0    1.0    1.0\n",
+              "cp        303.0    0.966997   1.032052    0.0    0.0    1.0    2.0    3.0\n",
+              "trestbps  303.0  131.623762  17.538143   94.0  120.0  130.0  140.0  200.0\n",
+              "chol      303.0  246.264026  51.830751  126.0  211.0  240.0  274.5  564.0\n",
+              "fbs       303.0    0.148515   0.356198    0.0    0.0    0.0    0.0    1.0\n",
+              "restecg   303.0    0.528053   0.525860    0.0    0.0    1.0    1.0    2.0\n",
+              "thalach   303.0  149.646865  22.905161   71.0  133.5  153.0  166.0  202.0\n",
+              "exang     303.0    0.326733   0.469794    0.0    0.0    0.0    1.0    1.0\n",
+              "oldpeak   303.0    1.039604   1.161075    0.0    0.0    0.8    1.6    6.2\n",
+              "slope     303.0    1.399340   0.616226    0.0    1.0    1.0    2.0    2.0\n",
+              "ca        303.0    0.729373   1.022606    0.0    0.0    0.0    1.0    4.0\n",
+              "thal      303.0    2.313531   0.612277    0.0    2.0    2.0    3.0    3.0\n",
+              "target    303.0    0.544554   0.498835    0.0    0.0    1.0    1.0    1.0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "a6ushlSP9EJP",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "366a429a-9d57-4ea8-bc8d-b51b0baf45fc"
+      },
+      "source": [
+        "# Display the Missing Values\n",
+        "\n",
+        "print(data.isna().sum())"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "age         0\n",
+            "sex         0\n",
+            "cp          0\n",
+            "trestbps    0\n",
+            "chol        0\n",
+            "fbs         0\n",
+            "restecg     0\n",
+            "thalach     0\n",
+            "exang       0\n",
+            "oldpeak     0\n",
+            "slope       0\n",
+            "ca          0\n",
+            "thal        0\n",
+            "target      0\n",
+            "dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IXOMCsIl9JpJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 612
+        },
+        "outputId": "7b01ed95-db2a-435c-f6e5-c8423976082c"
+      },
+      "source": [
+        "# calculate correlation matrix\n",
+        "\n",
+        "corr = data.corr()\n",
+        "plt.subplots(figsize=(15,10))\n",
+        "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, annot=True, cmap=sns.diverging_palette(220, 20, as_cmap=True))\n",
+        "sns.heatmap(corr, xticklabels=corr.columns,\n",
+        "            yticklabels=corr.columns, \n",
+        "            annot=True,\n",
+        "            cmap=sns.diverging_palette(220, 20, as_cmap=True))"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c953e9d10>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1080x720 with 3 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qqZ2RHNI9gFK"
+      },
+      "source": [
+        "Filtering data by positive & negative Heart Disease patient"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "n9lROx7X9T2W",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 314
+        },
+        "outputId": "ca94464a-e45e-4429-d316-efebe625f5d0"
+      },
+      "source": [
+        "# Filtering data by POSITIVE Heart Disease patient\n",
+        "pos_data = data[data['target']==1]\n",
+        "pos_data.describe()"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>cp</th>\n",
+              "      <th>trestbps</th>\n",
+              "      <th>chol</th>\n",
+              "      <th>fbs</th>\n",
+              "      <th>restecg</th>\n",
+              "      <th>thalach</th>\n",
+              "      <th>exang</th>\n",
+              "      <th>oldpeak</th>\n",
+              "      <th>slope</th>\n",
+              "      <th>ca</th>\n",
+              "      <th>thal</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>52.496970</td>\n",
+              "      <td>0.563636</td>\n",
+              "      <td>1.375758</td>\n",
+              "      <td>129.303030</td>\n",
+              "      <td>242.230303</td>\n",
+              "      <td>0.139394</td>\n",
+              "      <td>0.593939</td>\n",
+              "      <td>158.466667</td>\n",
+              "      <td>0.139394</td>\n",
+              "      <td>0.583030</td>\n",
+              "      <td>1.593939</td>\n",
+              "      <td>0.363636</td>\n",
+              "      <td>2.121212</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>9.550651</td>\n",
+              "      <td>0.497444</td>\n",
+              "      <td>0.952222</td>\n",
+              "      <td>16.169613</td>\n",
+              "      <td>53.552872</td>\n",
+              "      <td>0.347412</td>\n",
+              "      <td>0.504818</td>\n",
+              "      <td>19.174276</td>\n",
+              "      <td>0.347412</td>\n",
+              "      <td>0.780683</td>\n",
+              "      <td>0.593635</td>\n",
+              "      <td>0.848894</td>\n",
+              "      <td>0.465752</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>94.000000</td>\n",
+              "      <td>126.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>96.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>120.000000</td>\n",
+              "      <td>208.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>149.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>52.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>130.000000</td>\n",
+              "      <td>234.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>161.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.200000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>59.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>140.000000</td>\n",
+              "      <td>267.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>172.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>76.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>180.000000</td>\n",
+              "      <td>564.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>202.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>4.200000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>4.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              age         sex          cp  ...          ca        thal  target\n",
+              "count  165.000000  165.000000  165.000000  ...  165.000000  165.000000   165.0\n",
+              "mean    52.496970    0.563636    1.375758  ...    0.363636    2.121212     1.0\n",
+              "std      9.550651    0.497444    0.952222  ...    0.848894    0.465752     0.0\n",
+              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000     1.0\n",
+              "25%     44.000000    0.000000    1.000000  ...    0.000000    2.000000     1.0\n",
+              "50%     52.000000    1.000000    2.000000  ...    0.000000    2.000000     1.0\n",
+              "75%     59.000000    1.000000    2.000000  ...    0.000000    2.000000     1.0\n",
+              "max     76.000000    1.000000    3.000000  ...    4.000000    3.000000     1.0\n",
+              "\n",
+              "[8 rows x 14 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iNdi8cwN9wmU",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 314
+        },
+        "outputId": "1e819b7f-f8cc-4418-f4f7-d5c8c010c77e"
+      },
+      "source": [
+        "# Filtering data by NEGATIVE Heart Disease patient\n",
+        "pos_data = data[data['target']==0]\n",
+        "pos_data.describe()"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>cp</th>\n",
+              "      <th>trestbps</th>\n",
+              "      <th>chol</th>\n",
+              "      <th>fbs</th>\n",
+              "      <th>restecg</th>\n",
+              "      <th>thalach</th>\n",
+              "      <th>exang</th>\n",
+              "      <th>oldpeak</th>\n",
+              "      <th>slope</th>\n",
+              "      <th>ca</th>\n",
+              "      <th>thal</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>56.601449</td>\n",
+              "      <td>0.826087</td>\n",
+              "      <td>0.478261</td>\n",
+              "      <td>134.398551</td>\n",
+              "      <td>251.086957</td>\n",
+              "      <td>0.159420</td>\n",
+              "      <td>0.449275</td>\n",
+              "      <td>139.101449</td>\n",
+              "      <td>0.550725</td>\n",
+              "      <td>1.585507</td>\n",
+              "      <td>1.166667</td>\n",
+              "      <td>1.166667</td>\n",
+              "      <td>2.543478</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>7.962082</td>\n",
+              "      <td>0.380416</td>\n",
+              "      <td>0.905920</td>\n",
+              "      <td>18.729944</td>\n",
+              "      <td>49.454614</td>\n",
+              "      <td>0.367401</td>\n",
+              "      <td>0.541321</td>\n",
+              "      <td>22.598782</td>\n",
+              "      <td>0.499232</td>\n",
+              "      <td>1.300340</td>\n",
+              "      <td>0.561324</td>\n",
+              "      <td>1.043460</td>\n",
+              "      <td>0.684762</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>35.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>131.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>71.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>52.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>120.000000</td>\n",
+              "      <td>217.250000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>125.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.600000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>58.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>130.000000</td>\n",
+              "      <td>249.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>142.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.400000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>62.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>144.750000</td>\n",
+              "      <td>283.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>156.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.500000</td>\n",
+              "      <td>1.750000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>77.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>200.000000</td>\n",
+              "      <td>409.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>195.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>6.200000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>4.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              age         sex          cp  ...          ca        thal  target\n",
+              "count  138.000000  138.000000  138.000000  ...  138.000000  138.000000   138.0\n",
+              "mean    56.601449    0.826087    0.478261  ...    1.166667    2.543478     0.0\n",
+              "std      7.962082    0.380416    0.905920  ...    1.043460    0.684762     0.0\n",
+              "min     35.000000    0.000000    0.000000  ...    0.000000    0.000000     0.0\n",
+              "25%     52.000000    1.000000    0.000000  ...    0.000000    2.000000     0.0\n",
+              "50%     58.000000    1.000000    0.000000  ...    1.000000    3.000000     0.0\n",
+              "75%     62.000000    1.000000    0.000000  ...    2.000000    3.000000     0.0\n",
+              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000     0.0\n",
+              "\n",
+              "[8 rows x 14 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DbzJPYQV9lPu"
+      },
+      "source": [
+        "Assign the 13 features to X, & the last column to our classification predictor, y"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FjMSVs94960R"
+      },
+      "source": [
+        "X = data.iloc[:, :-1].values\n",
+        "y = data.iloc[:, -1].values"
+      ],
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kYwABWTb-AJx"
+      },
+      "source": [
+        "Split: the data set into the Training set and Test set"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DlgSSiYE99tk"
+      },
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_test, y_train, y_test = train_test_split(X,y,test_size = 0.2, random_state = 1)"
+      ],
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MuoZklrK-FJY"
+      },
+      "source": [
+        "Normalize: Standardizing the data a mean of 0 and a standard deviation of 1."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rGfWqZir-LLs"
+      },
+      "source": [
+        "from sklearn.preprocessing import StandardScaler\n",
+        "sc = StandardScaler()\n",
+        "x_train = sc.fit_transform(x_train)\n",
+        "x_test = sc.transform(x_test)"
+      ],
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CwBqTSrI-WVv"
+      },
+      "source": [
+        "Modeling /Training"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xk0Uem6P-qgq"
+      },
+      "source": [
+        "Model: Random Forest\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "JaSaSKDT-qE-",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bbcc9de5-0a3e-4a70-9eb9-3530cc7803e0"
+      },
+      "source": [
+        "from sklearn.metrics import classification_report \n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "\n",
+        "model3 = RandomForestClassifier(random_state=1)# get instance of model\n",
+        "model3.fit(x_train, y_train) # Train/Fit model \n",
+        "\n",
+        "y_pred3 = model3.predict(x_test) # get y predictions\n",
+        "print(classification_report(y_test, y_pred3)) # output accuracy"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.88      0.70      0.78        30\n",
+            "           1       0.76      0.90      0.82        31\n",
+            "\n",
+            "    accuracy                           0.80        61\n",
+            "   macro avg       0.82      0.80      0.80        61\n",
+            "weighted avg       0.81      0.80      0.80        61\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NYkRl2Et-1vo"
+      },
+      "source": [
+        "Making the Confusion Matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qH0hRD8j-3m9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f8f38c70-ce4c-4824-9ba3-13d9e6b06f5b"
+      },
+      "source": [
+        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+        "cm = confusion_matrix(y_test, y_pred3)\n",
+        "print(cm)\n",
+        "accuracy_score(y_test, y_pred3)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[21  9]\n",
+            " [ 3 28]]\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8032786885245902"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fu7KQOnjzY6g",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "87984799-bb19-4075-de5c-d83033940f4c"
+      },
+      "source": [
+        "TN = cm[0][0]\n",
+        "FN = cm[1][0]\n",
+        "TP = cm[1][1]\n",
+        "FP = cm[0][1]\n",
+        "\n",
+        "specificity = (TN/TN+FP)\n",
+        "\n",
+        "print ('Specificity = ', specificity)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Specificity =  10.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Vt5yCe496RoC",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "082a4a24-d607-44b9-d067-f69f77657a84"
+      },
+      "source": [
+        "senstivity = (TP/TP+FN)\n",
+        "\n",
+        "print ('Senstivity = ', senstivity)"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Senstivity =  4.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HrNkVFg8-_hj"
+      },
+      "source": [
+        "Feature Importance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qCRNG8uR-6mX",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9cbe2af3-6685-4e5e-9c62-6124190834ae"
+      },
+      "source": [
+        "# get importance\n",
+        "importance = model3.feature_importances_\n",
+        "\n",
+        "# summarize feature importance\n",
+        "for i,v in enumerate(importance):\n",
+        "    print('Feature: %0d, Score: %.5f' % (i,v))"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Feature: 0, Score: 0.07814\n",
+            "Feature: 1, Score: 0.04206\n",
+            "Feature: 2, Score: 0.16580\n",
+            "Feature: 3, Score: 0.07477\n",
+            "Feature: 4, Score: 0.07587\n",
+            "Feature: 5, Score: 0.00828\n",
+            "Feature: 6, Score: 0.02014\n",
+            "Feature: 7, Score: 0.12772\n",
+            "Feature: 8, Score: 0.06950\n",
+            "Feature: 9, Score: 0.09957\n",
+            "Feature: 10, Score: 0.04677\n",
+            "Feature: 11, Score: 0.11667\n",
+            "Feature: 12, Score: 0.07473\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tUtHmfsu_EOF",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 282
+        },
+        "outputId": "3ccc0576-c4dd-41ba-e83e-cac8040ced12"
+      },
+      "source": [
+        "index= data.columns[:-1]\n",
+        "importance = pd.Series(model3.feature_importances_, index=index)\n",
+        "importance.nlargest(13).plot(kind='barh', colormap='winter')"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c8d92dbd0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 7d2c3925df5439931c72e05f2dd0e7c93a02b7f8 Mon Sep 17 00:00:00 2001
From: Kainat-Mahar <72600530+Kainat-Mahar@users.noreply.github.com>
Date: Fri, 22 Oct 2021 15:27:56 +0500
Subject: [PATCH 063/232] Delete
 Classification_using_Random_Forest_Algorithm.ipynb

---
 ...cation_using_Random_Forest_Algorithm.ipynb | 1355 -----------------
 1 file changed, 1355 deletions(-)
 delete mode 100644 Data Science/Classification_using_Random_Forest_Algorithm.ipynb

diff --git a/Data Science/Classification_using_Random_Forest_Algorithm.ipynb b/Data Science/Classification_using_Random_Forest_Algorithm.ipynb
deleted file mode 100644
index ef5e38c..0000000
--- a/Data Science/Classification_using_Random_Forest_Algorithm.ipynb	
+++ /dev/null
@@ -1,1355 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "Classification using Random Forest Algorithm.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "HbbGwEA47kDj"
-      },
-      "source": [
-        "Importing Libraries"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "roYk-Q3s6yVd"
-      },
-      "source": [
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import matplotlib as plt\n",
-        "import seaborn as sns\n",
-        "import matplotlib.pyplot as plt"
-      ],
-      "execution_count": 1,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "7v8I08N58jcA"
-      },
-      "source": [
-        "Data Wrangling"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Ttq0PCrZ8i_d",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 202
-        },
-        "outputId": "d2a6d459-e354-475f-d2c5-ef08982f82d0"
-      },
-      "source": [
-        "data = pd.read_csv('dataset.csv')\n",
-        "\n",
-        "data.head(5)"
-      ],
-      "execution_count": 2,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>age</th>\n",
-              "      <th>sex</th>\n",
-              "      <th>cp</th>\n",
-              "      <th>trestbps</th>\n",
-              "      <th>chol</th>\n",
-              "      <th>fbs</th>\n",
-              "      <th>restecg</th>\n",
-              "      <th>thalach</th>\n",
-              "      <th>exang</th>\n",
-              "      <th>oldpeak</th>\n",
-              "      <th>slope</th>\n",
-              "      <th>ca</th>\n",
-              "      <th>thal</th>\n",
-              "      <th>target</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0</th>\n",
-              "      <td>63</td>\n",
-              "      <td>1</td>\n",
-              "      <td>3</td>\n",
-              "      <td>145</td>\n",
-              "      <td>233</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0</td>\n",
-              "      <td>150</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2.3</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1</th>\n",
-              "      <td>37</td>\n",
-              "      <td>1</td>\n",
-              "      <td>2</td>\n",
-              "      <td>130</td>\n",
-              "      <td>250</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>187</td>\n",
-              "      <td>0</td>\n",
-              "      <td>3.5</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2</th>\n",
-              "      <td>41</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>130</td>\n",
-              "      <td>204</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>172</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1.4</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3</th>\n",
-              "      <td>56</td>\n",
-              "      <td>1</td>\n",
-              "      <td>1</td>\n",
-              "      <td>120</td>\n",
-              "      <td>236</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>178</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0.8</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4</th>\n",
-              "      <td>57</td>\n",
-              "      <td>0</td>\n",
-              "      <td>0</td>\n",
-              "      <td>120</td>\n",
-              "      <td>354</td>\n",
-              "      <td>0</td>\n",
-              "      <td>1</td>\n",
-              "      <td>163</td>\n",
-              "      <td>1</td>\n",
-              "      <td>0.6</td>\n",
-              "      <td>2</td>\n",
-              "      <td>0</td>\n",
-              "      <td>2</td>\n",
-              "      <td>1</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
-              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
-              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
-              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
-              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
-              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
-              "\n",
-              "[5 rows x 14 columns]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 2
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "8PJwi9rc8tp-",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "4e8f191b-6599-496e-b97b-8fcbedbc7b22"
-      },
-      "source": [
-        "print(\"(Rows, columns): \" + str(data.shape))\n",
-        "data.columns "
-      ],
-      "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "(Rows, columns): (303, 14)\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
-              "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
-              "      dtype='object')"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 3
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "PxY79ivL80gB",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "73b9d73d-e643-4602-ac52-c8df5d5a8413"
-      },
-      "source": [
-        "data.nunique(axis=0)# returns the number of unique values for each variable."
-      ],
-      "execution_count": 4,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "age          41\n",
-              "sex           2\n",
-              "cp            4\n",
-              "trestbps     49\n",
-              "chol        152\n",
-              "fbs           2\n",
-              "restecg       3\n",
-              "thalach      91\n",
-              "exang         2\n",
-              "oldpeak      40\n",
-              "slope         3\n",
-              "ca            5\n",
-              "thal          4\n",
-              "target        2\n",
-              "dtype: int64"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 4
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "wn63NFgG88dd",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 477
-        },
-        "outputId": "83fe2164-71d8-438a-f38f-6076eb0512c6"
-      },
-      "source": [
-        "#summarizes the count, mean, standard deviation, min, and max for numeric variables.\n",
-        "data.describe().transpose()"
-      ],
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>count</th>\n",
-              "      <th>mean</th>\n",
-              "      <th>std</th>\n",
-              "      <th>min</th>\n",
-              "      <th>25%</th>\n",
-              "      <th>50%</th>\n",
-              "      <th>75%</th>\n",
-              "      <th>max</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>age</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>54.366337</td>\n",
-              "      <td>9.082101</td>\n",
-              "      <td>29.0</td>\n",
-              "      <td>47.5</td>\n",
-              "      <td>55.0</td>\n",
-              "      <td>61.0</td>\n",
-              "      <td>77.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>sex</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>0.683168</td>\n",
-              "      <td>0.466011</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>cp</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>0.966997</td>\n",
-              "      <td>1.032052</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>3.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>trestbps</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>131.623762</td>\n",
-              "      <td>17.538143</td>\n",
-              "      <td>94.0</td>\n",
-              "      <td>120.0</td>\n",
-              "      <td>130.0</td>\n",
-              "      <td>140.0</td>\n",
-              "      <td>200.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>chol</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>246.264026</td>\n",
-              "      <td>51.830751</td>\n",
-              "      <td>126.0</td>\n",
-              "      <td>211.0</td>\n",
-              "      <td>240.0</td>\n",
-              "      <td>274.5</td>\n",
-              "      <td>564.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>fbs</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>0.148515</td>\n",
-              "      <td>0.356198</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>restecg</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>0.528053</td>\n",
-              "      <td>0.525860</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>thalach</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>149.646865</td>\n",
-              "      <td>22.905161</td>\n",
-              "      <td>71.0</td>\n",
-              "      <td>133.5</td>\n",
-              "      <td>153.0</td>\n",
-              "      <td>166.0</td>\n",
-              "      <td>202.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>exang</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>0.326733</td>\n",
-              "      <td>0.469794</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>oldpeak</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>1.039604</td>\n",
-              "      <td>1.161075</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.8</td>\n",
-              "      <td>1.6</td>\n",
-              "      <td>6.2</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>slope</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>1.399340</td>\n",
-              "      <td>0.616226</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>2.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>ca</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>0.729373</td>\n",
-              "      <td>1.022606</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>4.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>thal</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>2.313531</td>\n",
-              "      <td>0.612277</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>2.0</td>\n",
-              "      <td>3.0</td>\n",
-              "      <td>3.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>target</th>\n",
-              "      <td>303.0</td>\n",
-              "      <td>0.544554</td>\n",
-              "      <td>0.498835</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "          count        mean        std    min    25%    50%    75%    max\n",
-              "age       303.0   54.366337   9.082101   29.0   47.5   55.0   61.0   77.0\n",
-              "sex       303.0    0.683168   0.466011    0.0    0.0    1.0    1.0    1.0\n",
-              "cp        303.0    0.966997   1.032052    0.0    0.0    1.0    2.0    3.0\n",
-              "trestbps  303.0  131.623762  17.538143   94.0  120.0  130.0  140.0  200.0\n",
-              "chol      303.0  246.264026  51.830751  126.0  211.0  240.0  274.5  564.0\n",
-              "fbs       303.0    0.148515   0.356198    0.0    0.0    0.0    0.0    1.0\n",
-              "restecg   303.0    0.528053   0.525860    0.0    0.0    1.0    1.0    2.0\n",
-              "thalach   303.0  149.646865  22.905161   71.0  133.5  153.0  166.0  202.0\n",
-              "exang     303.0    0.326733   0.469794    0.0    0.0    0.0    1.0    1.0\n",
-              "oldpeak   303.0    1.039604   1.161075    0.0    0.0    0.8    1.6    6.2\n",
-              "slope     303.0    1.399340   0.616226    0.0    1.0    1.0    2.0    2.0\n",
-              "ca        303.0    0.729373   1.022606    0.0    0.0    0.0    1.0    4.0\n",
-              "thal      303.0    2.313531   0.612277    0.0    2.0    2.0    3.0    3.0\n",
-              "target    303.0    0.544554   0.498835    0.0    0.0    1.0    1.0    1.0"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "a6ushlSP9EJP",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "366a429a-9d57-4ea8-bc8d-b51b0baf45fc"
-      },
-      "source": [
-        "# Display the Missing Values\n",
-        "\n",
-        "print(data.isna().sum())"
-      ],
-      "execution_count": 6,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "age         0\n",
-            "sex         0\n",
-            "cp          0\n",
-            "trestbps    0\n",
-            "chol        0\n",
-            "fbs         0\n",
-            "restecg     0\n",
-            "thalach     0\n",
-            "exang       0\n",
-            "oldpeak     0\n",
-            "slope       0\n",
-            "ca          0\n",
-            "thal        0\n",
-            "target      0\n",
-            "dtype: int64\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "IXOMCsIl9JpJ",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 612
-        },
-        "outputId": "7b01ed95-db2a-435c-f6e5-c8423976082c"
-      },
-      "source": [
-        "# calculate correlation matrix\n",
-        "\n",
-        "corr = data.corr()\n",
-        "plt.subplots(figsize=(15,10))\n",
-        "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, annot=True, cmap=sns.diverging_palette(220, 20, as_cmap=True))\n",
-        "sns.heatmap(corr, xticklabels=corr.columns,\n",
-        "            yticklabels=corr.columns, \n",
-        "            annot=True,\n",
-        "            cmap=sns.diverging_palette(220, 20, as_cmap=True))"
-      ],
-      "execution_count": 7,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c953e9d10>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 7
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 1080x720 with 3 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "qqZ2RHNI9gFK"
-      },
-      "source": [
-        "Filtering data by positive & negative Heart Disease patient"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "n9lROx7X9T2W",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 314
-        },
-        "outputId": "ca94464a-e45e-4429-d316-efebe625f5d0"
-      },
-      "source": [
-        "# Filtering data by POSITIVE Heart Disease patient\n",
-        "pos_data = data[data['target']==1]\n",
-        "pos_data.describe()"
-      ],
-      "execution_count": 8,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>age</th>\n",
-              "      <th>sex</th>\n",
-              "      <th>cp</th>\n",
-              "      <th>trestbps</th>\n",
-              "      <th>chol</th>\n",
-              "      <th>fbs</th>\n",
-              "      <th>restecg</th>\n",
-              "      <th>thalach</th>\n",
-              "      <th>exang</th>\n",
-              "      <th>oldpeak</th>\n",
-              "      <th>slope</th>\n",
-              "      <th>ca</th>\n",
-              "      <th>thal</th>\n",
-              "      <th>target</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.000000</td>\n",
-              "      <td>165.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>52.496970</td>\n",
-              "      <td>0.563636</td>\n",
-              "      <td>1.375758</td>\n",
-              "      <td>129.303030</td>\n",
-              "      <td>242.230303</td>\n",
-              "      <td>0.139394</td>\n",
-              "      <td>0.593939</td>\n",
-              "      <td>158.466667</td>\n",
-              "      <td>0.139394</td>\n",
-              "      <td>0.583030</td>\n",
-              "      <td>1.593939</td>\n",
-              "      <td>0.363636</td>\n",
-              "      <td>2.121212</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>9.550651</td>\n",
-              "      <td>0.497444</td>\n",
-              "      <td>0.952222</td>\n",
-              "      <td>16.169613</td>\n",
-              "      <td>53.552872</td>\n",
-              "      <td>0.347412</td>\n",
-              "      <td>0.504818</td>\n",
-              "      <td>19.174276</td>\n",
-              "      <td>0.347412</td>\n",
-              "      <td>0.780683</td>\n",
-              "      <td>0.593635</td>\n",
-              "      <td>0.848894</td>\n",
-              "      <td>0.465752</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>min</th>\n",
-              "      <td>29.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>94.000000</td>\n",
-              "      <td>126.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>96.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>25%</th>\n",
-              "      <td>44.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>120.000000</td>\n",
-              "      <td>208.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>149.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>50%</th>\n",
-              "      <td>52.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>130.000000</td>\n",
-              "      <td>234.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>161.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.200000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>75%</th>\n",
-              "      <td>59.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>140.000000</td>\n",
-              "      <td>267.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>172.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>max</th>\n",
-              "      <td>76.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>180.000000</td>\n",
-              "      <td>564.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>202.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>4.200000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>4.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>1.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "              age         sex          cp  ...          ca        thal  target\n",
-              "count  165.000000  165.000000  165.000000  ...  165.000000  165.000000   165.0\n",
-              "mean    52.496970    0.563636    1.375758  ...    0.363636    2.121212     1.0\n",
-              "std      9.550651    0.497444    0.952222  ...    0.848894    0.465752     0.0\n",
-              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000     1.0\n",
-              "25%     44.000000    0.000000    1.000000  ...    0.000000    2.000000     1.0\n",
-              "50%     52.000000    1.000000    2.000000  ...    0.000000    2.000000     1.0\n",
-              "75%     59.000000    1.000000    2.000000  ...    0.000000    2.000000     1.0\n",
-              "max     76.000000    1.000000    3.000000  ...    4.000000    3.000000     1.0\n",
-              "\n",
-              "[8 rows x 14 columns]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 8
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "iNdi8cwN9wmU",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 314
-        },
-        "outputId": "1e819b7f-f8cc-4418-f4f7-d5c8c010c77e"
-      },
-      "source": [
-        "# Filtering data by NEGATIVE Heart Disease patient\n",
-        "pos_data = data[data['target']==0]\n",
-        "pos_data.describe()"
-      ],
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>age</th>\n",
-              "      <th>sex</th>\n",
-              "      <th>cp</th>\n",
-              "      <th>trestbps</th>\n",
-              "      <th>chol</th>\n",
-              "      <th>fbs</th>\n",
-              "      <th>restecg</th>\n",
-              "      <th>thalach</th>\n",
-              "      <th>exang</th>\n",
-              "      <th>oldpeak</th>\n",
-              "      <th>slope</th>\n",
-              "      <th>ca</th>\n",
-              "      <th>thal</th>\n",
-              "      <th>target</th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>count</th>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.000000</td>\n",
-              "      <td>138.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>mean</th>\n",
-              "      <td>56.601449</td>\n",
-              "      <td>0.826087</td>\n",
-              "      <td>0.478261</td>\n",
-              "      <td>134.398551</td>\n",
-              "      <td>251.086957</td>\n",
-              "      <td>0.159420</td>\n",
-              "      <td>0.449275</td>\n",
-              "      <td>139.101449</td>\n",
-              "      <td>0.550725</td>\n",
-              "      <td>1.585507</td>\n",
-              "      <td>1.166667</td>\n",
-              "      <td>1.166667</td>\n",
-              "      <td>2.543478</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>std</th>\n",
-              "      <td>7.962082</td>\n",
-              "      <td>0.380416</td>\n",
-              "      <td>0.905920</td>\n",
-              "      <td>18.729944</td>\n",
-              "      <td>49.454614</td>\n",
-              "      <td>0.367401</td>\n",
-              "      <td>0.541321</td>\n",
-              "      <td>22.598782</td>\n",
-              "      <td>0.499232</td>\n",
-              "      <td>1.300340</td>\n",
-              "      <td>0.561324</td>\n",
-              "      <td>1.043460</td>\n",
-              "      <td>0.684762</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>min</th>\n",
-              "      <td>35.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>100.000000</td>\n",
-              "      <td>131.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>71.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>25%</th>\n",
-              "      <td>52.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>120.000000</td>\n",
-              "      <td>217.250000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>125.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.600000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>50%</th>\n",
-              "      <td>58.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>130.000000</td>\n",
-              "      <td>249.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>142.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>1.400000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>75%</th>\n",
-              "      <td>62.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>144.750000</td>\n",
-              "      <td>283.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>156.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>2.500000</td>\n",
-              "      <td>1.750000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>max</th>\n",
-              "      <td>77.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>200.000000</td>\n",
-              "      <td>409.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>195.000000</td>\n",
-              "      <td>1.000000</td>\n",
-              "      <td>6.200000</td>\n",
-              "      <td>2.000000</td>\n",
-              "      <td>4.000000</td>\n",
-              "      <td>3.000000</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "              age         sex          cp  ...          ca        thal  target\n",
-              "count  138.000000  138.000000  138.000000  ...  138.000000  138.000000   138.0\n",
-              "mean    56.601449    0.826087    0.478261  ...    1.166667    2.543478     0.0\n",
-              "std      7.962082    0.380416    0.905920  ...    1.043460    0.684762     0.0\n",
-              "min     35.000000    0.000000    0.000000  ...    0.000000    0.000000     0.0\n",
-              "25%     52.000000    1.000000    0.000000  ...    0.000000    2.000000     0.0\n",
-              "50%     58.000000    1.000000    0.000000  ...    1.000000    3.000000     0.0\n",
-              "75%     62.000000    1.000000    0.000000  ...    2.000000    3.000000     0.0\n",
-              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000     0.0\n",
-              "\n",
-              "[8 rows x 14 columns]"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 9
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DbzJPYQV9lPu"
-      },
-      "source": [
-        "Assign the 13 features to X, & the last column to our classification predictor, y"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "FjMSVs94960R"
-      },
-      "source": [
-        "X = data.iloc[:, :-1].values\n",
-        "y = data.iloc[:, -1].values"
-      ],
-      "execution_count": 10,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "kYwABWTb-AJx"
-      },
-      "source": [
-        "Split: the data set into the Training set and Test set"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "DlgSSiYE99tk"
-      },
-      "source": [
-        "from sklearn.model_selection import train_test_split\n",
-        "x_train, x_test, y_train, y_test = train_test_split(X,y,test_size = 0.2, random_state = 1)"
-      ],
-      "execution_count": 11,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "MuoZklrK-FJY"
-      },
-      "source": [
-        "Normalize: Standardizing the data a mean of 0 and a standard deviation of 1."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "rGfWqZir-LLs"
-      },
-      "source": [
-        "from sklearn.preprocessing import StandardScaler\n",
-        "sc = StandardScaler()\n",
-        "x_train = sc.fit_transform(x_train)\n",
-        "x_test = sc.transform(x_test)"
-      ],
-      "execution_count": 12,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "CwBqTSrI-WVv"
-      },
-      "source": [
-        "Modeling /Training"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "xk0Uem6P-qgq"
-      },
-      "source": [
-        "Model: Random Forest\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "JaSaSKDT-qE-",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "bbcc9de5-0a3e-4a70-9eb9-3530cc7803e0"
-      },
-      "source": [
-        "from sklearn.metrics import classification_report \n",
-        "from sklearn.ensemble import RandomForestClassifier\n",
-        "\n",
-        "model3 = RandomForestClassifier(random_state=1)# get instance of model\n",
-        "model3.fit(x_train, y_train) # Train/Fit model \n",
-        "\n",
-        "y_pred3 = model3.predict(x_test) # get y predictions\n",
-        "print(classification_report(y_test, y_pred3)) # output accuracy"
-      ],
-      "execution_count": 13,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "              precision    recall  f1-score   support\n",
-            "\n",
-            "           0       0.88      0.70      0.78        30\n",
-            "           1       0.76      0.90      0.82        31\n",
-            "\n",
-            "    accuracy                           0.80        61\n",
-            "   macro avg       0.82      0.80      0.80        61\n",
-            "weighted avg       0.81      0.80      0.80        61\n",
-            "\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "NYkRl2Et-1vo"
-      },
-      "source": [
-        "Making the Confusion Matrix"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "qH0hRD8j-3m9",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "f8f38c70-ce4c-4824-9ba3-13d9e6b06f5b"
-      },
-      "source": [
-        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
-        "cm = confusion_matrix(y_test, y_pred3)\n",
-        "print(cm)\n",
-        "accuracy_score(y_test, y_pred3)"
-      ],
-      "execution_count": 14,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "[[21  9]\n",
-            " [ 3 28]]\n"
-          ]
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "0.8032786885245902"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 14
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "fu7KQOnjzY6g",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "87984799-bb19-4075-de5c-d83033940f4c"
-      },
-      "source": [
-        "TN = cm[0][0]\n",
-        "FN = cm[1][0]\n",
-        "TP = cm[1][1]\n",
-        "FP = cm[0][1]\n",
-        "\n",
-        "specificity = (TN/TN+FP)\n",
-        "\n",
-        "print ('Specificity = ', specificity)"
-      ],
-      "execution_count": 15,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Specificity =  10.0\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Vt5yCe496RoC",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "082a4a24-d607-44b9-d067-f69f77657a84"
-      },
-      "source": [
-        "senstivity = (TP/TP+FN)\n",
-        "\n",
-        "print ('Senstivity = ', senstivity)"
-      ],
-      "execution_count": 16,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Senstivity =  4.0\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "HrNkVFg8-_hj"
-      },
-      "source": [
-        "Feature Importance"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "qCRNG8uR-6mX",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "9cbe2af3-6685-4e5e-9c62-6124190834ae"
-      },
-      "source": [
-        "# get importance\n",
-        "importance = model3.feature_importances_\n",
-        "\n",
-        "# summarize feature importance\n",
-        "for i,v in enumerate(importance):\n",
-        "    print('Feature: %0d, Score: %.5f' % (i,v))"
-      ],
-      "execution_count": 17,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Feature: 0, Score: 0.07814\n",
-            "Feature: 1, Score: 0.04206\n",
-            "Feature: 2, Score: 0.16580\n",
-            "Feature: 3, Score: 0.07477\n",
-            "Feature: 4, Score: 0.07587\n",
-            "Feature: 5, Score: 0.00828\n",
-            "Feature: 6, Score: 0.02014\n",
-            "Feature: 7, Score: 0.12772\n",
-            "Feature: 8, Score: 0.06950\n",
-            "Feature: 9, Score: 0.09957\n",
-            "Feature: 10, Score: 0.04677\n",
-            "Feature: 11, Score: 0.11667\n",
-            "Feature: 12, Score: 0.07473\n"
-          ]
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "tUtHmfsu_EOF",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 282
-        },
-        "outputId": "3ccc0576-c4dd-41ba-e83e-cac8040ced12"
-      },
-      "source": [
-        "index= data.columns[:-1]\n",
-        "importance = pd.Series(model3.feature_importances_, index=index)\n",
-        "importance.nlargest(13).plot(kind='barh', colormap='winter')"
-      ],
-      "execution_count": 18,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c8d92dbd0>"
-            ]
-          },
-          "metadata": {},
-          "execution_count": 18
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": "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\n",
-            "text/plain": [
-              "<Figure size 432x288 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "needs_background": "light"
-          }
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file

From eb6f8533fda58444719fb2300049fd17a16f9048 Mon Sep 17 00:00:00 2001
From: Kainat-Mahar <72600530+Kainat-Mahar@users.noreply.github.com>
Date: Fri, 22 Oct 2021 15:28:22 +0500
Subject: [PATCH 064/232] Delete Bitcoin__Price_Prediction.ipynb

---
 .../Bitcoin__Price_Prediction.ipynb           | 1027 -----------------
 1 file changed, 1027 deletions(-)
 delete mode 100644 Machine Learning/Bitcoin__Price_Prediction.ipynb

diff --git a/Machine Learning/Bitcoin__Price_Prediction.ipynb b/Machine Learning/Bitcoin__Price_Prediction.ipynb
deleted file mode 100644
index 10cc954..0000000
--- a/Machine Learning/Bitcoin__Price_Prediction.ipynb	
+++ /dev/null
@@ -1,1027 +0,0 @@
-{
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "name": "Bitcoin _Price_Prediction.ipynb",
-      "provenance": [],
-      "collapsed_sections": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    }
-  },
-  "cells": [
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "FmMO__YhieOb"
-      },
-      "source": [
-        "import json\n",
-        "import requests\n",
-        "import tensorflow as tf\n",
-        "from tensorflow import  keras\n",
-        "from tensorflow.keras import layers\n",
-        "#from tensorflow.contrib import lite\n",
-        "from keras.models import Sequential\n",
-        "from keras.layers import Activation, Dense, Dropout, LSTM\n",
-        "from keras.callbacks import EarlyStopping\n",
-        "import matplotlib.pyplot as plt\n",
-        "import plotly.offline as py\n",
-        "import plotly.graph_objs as go\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import seaborn as sns\n",
-        "from sklearn.metrics import mean_absolute_error\n",
-        "%matplotlib inline"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "FBVKRHGfigX6"
-      },
-      "source": [
-        "endpoint = 'https://min-api.cryptocompare.com/data/histoday'\n",
-        "res = requests.get(endpoint + '?fsym=BTC&tsym=USD&limit=500')\n",
-        "data = pd.DataFrame(json.loads(res.content)['Data'])"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "IBY6ytPNik0L",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 227
-        },
-        "outputId": "9a29c462-99a5-4081-8849-f19db1a131f5"
-      },
-      "source": [
-        "data = data.set_index('time')\n",
-        "data.index = pd.to_datetime(data.index, unit='s')\n",
-        "data.head(5)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/html": [
-              "<div>\n",
-              "<style scoped>\n",
-              "    .dataframe tbody tr th:only-of-type {\n",
-              "        vertical-align: middle;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe tbody tr th {\n",
-              "        vertical-align: top;\n",
-              "    }\n",
-              "\n",
-              "    .dataframe thead th {\n",
-              "        text-align: right;\n",
-              "    }\n",
-              "</style>\n",
-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th></th>\n",
-              "      <th>high</th>\n",
-              "      <th>low</th>\n",
-              "      <th>open</th>\n",
-              "      <th>volumefrom</th>\n",
-              "      <th>volumeto</th>\n",
-              "      <th>close</th>\n",
-              "      <th>conversionType</th>\n",
-              "      <th>conversionSymbol</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>time</th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>2019-12-21</th>\n",
-              "      <td>7213.86</td>\n",
-              "      <td>7132.82</td>\n",
-              "      <td>7203.41</td>\n",
-              "      <td>12101.12</td>\n",
-              "      <td>8.686484e+07</td>\n",
-              "      <td>7159.47</td>\n",
-              "      <td>direct</td>\n",
-              "      <td></td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2019-12-22</th>\n",
-              "      <td>7527.40</td>\n",
-              "      <td>7143.17</td>\n",
-              "      <td>7159.47</td>\n",
-              "      <td>27325.26</td>\n",
-              "      <td>2.005866e+08</td>\n",
-              "      <td>7517.58</td>\n",
-              "      <td>direct</td>\n",
-              "      <td></td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2019-12-23</th>\n",
-              "      <td>7694.65</td>\n",
-              "      <td>7275.28</td>\n",
-              "      <td>7517.58</td>\n",
-              "      <td>44770.93</td>\n",
-              "      <td>3.364250e+08</td>\n",
-              "      <td>7326.60</td>\n",
-              "      <td>direct</td>\n",
-              "      <td></td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2019-12-24</th>\n",
-              "      <td>7429.42</td>\n",
-              "      <td>7175.50</td>\n",
-              "      <td>7326.60</td>\n",
-              "      <td>27770.75</td>\n",
-              "      <td>2.030134e+08</td>\n",
-              "      <td>7260.91</td>\n",
-              "      <td>direct</td>\n",
-              "      <td></td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2019-12-25</th>\n",
-              "      <td>7280.52</td>\n",
-              "      <td>7132.30</td>\n",
-              "      <td>7260.91</td>\n",
-              "      <td>15012.21</td>\n",
-              "      <td>1.085133e+08</td>\n",
-              "      <td>7202.72</td>\n",
-              "      <td>direct</td>\n",
-              "      <td></td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "               high      low     open  ...    close  conversionType  conversionSymbol\n",
-              "time                                   ...                                           \n",
-              "2019-12-21  7213.86  7132.82  7203.41  ...  7159.47          direct                  \n",
-              "2019-12-22  7527.40  7143.17  7159.47  ...  7517.58          direct                  \n",
-              "2019-12-23  7694.65  7275.28  7517.58  ...  7326.60          direct                  \n",
-              "2019-12-24  7429.42  7175.50  7326.60  ...  7260.91          direct                  \n",
-              "2019-12-25  7280.52  7132.30  7260.91  ...  7202.72          direct                  \n",
-              "\n",
-              "[5 rows x 8 columns]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 3
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "AnPPwQsXiliC",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "21546cfe-08d4-49b0-e5bd-f06af964fb6e"
-      },
-      "source": [
-        "target_col = data['close']\n",
-        "len(target_col)\n",
-        "target_col"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "time\n",
-              "2019-12-21     7159.47\n",
-              "2019-12-22     7517.58\n",
-              "2019-12-23     7326.60\n",
-              "2019-12-24     7260.91\n",
-              "2019-12-25     7202.72\n",
-              "                ...   \n",
-              "2021-04-30    57757.97\n",
-              "2021-05-01    57843.16\n",
-              "2021-05-02    56623.51\n",
-              "2021-05-03    57195.07\n",
-              "2021-05-04    56108.65\n",
-              "Name: close, Length: 501, dtype: float64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 4
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "LakZtj3riThB",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "78ff2ab8-b313-45fe-d53c-7e99658abe1f"
-      },
-      "source": [
-        "target_col.describe()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "count      501.000000\n",
-              "mean     20289.575749\n",
-              "std      17327.126371\n",
-              "min       4916.780000\n",
-              "25%       9160.390000\n",
-              "50%      10929.400000\n",
-              "75%      28889.610000\n",
-              "max      63558.480000\n",
-              "Name: close, dtype: float64"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 5
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "8mK8uToX7uKJ",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 392
-        },
-        "outputId": "51c951a7-cfbf-47ce-b2e2-c932eda94e5c"
-      },
-      "source": [
-        "plt.rcParams['figure.figsize'] = [10,6]\n",
-        "plt.plot(target_col)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "[<matplotlib.lines.Line2D at 0x7f518cd31e90>]"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 6
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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\n",
-            "text/plain": [
-              "<Figure size 720x432 with 1 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": [],
-            "needs_background": "light"
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "KPzobcXnqlyq"
-      },
-      "source": [
-        "from sklearn.preprocessing import MinMaxScaler\n",
-        "scaler=MinMaxScaler(feature_range=(0,1))\n",
-        "target_col=scaler.fit_transform(np.array(target_col).reshape(-1,1))"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "3ez8_5IvogUP"
-      },
-      "source": [
-        "##splitting dataset into train and test split\n",
-        "training_size=int(len(target_col)*0.80)\n",
-        "test_size=len(target_col)-training_size\n",
-        "train_data,test_data=target_col[0:training_size,:],target_col[training_size:len(target_col),:1]"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "FMon8VWLr6jO"
-      },
-      "source": [
-        "# convert an array of values into a dataset matrix\n",
-        "def create_dataset(dataset, time_step=1):\n",
-        "\tdataX, dataY = [], []\n",
-        "\tfor i in range(len(dataset)-time_step-1):\n",
-        "\t\ta = dataset[i:(i+time_step), 0]   ###i=0, 0,1,2,3-----99   100 \n",
-        "\t\tdataX.append(a)\n",
-        "\t\tdataY.append(dataset[i + time_step, 0])\n",
-        "\treturn np.array(dataX), np.array(dataY)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "583kizUCsA7u"
-      },
-      "source": [
-        "# reshape into X=t,t+1,t+2,t+3 and Y=t+4\n",
-        "time_step=1\n",
-        "X_train, y_train = create_dataset(train_data, time_step)\n",
-        "X_test, ytest = create_dataset(test_data, time_step)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "ePpTIUHOkAt3",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "9700ea2a-1782-4b5b-fc1a-e87a0a881a7e"
-      },
-      "source": [
-        "print(X_train.shape), print(y_train.shape)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "(398, 1)\n",
-            "(398,)\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(None, None)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 11
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "CNaEHZOwkIj3",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "33081e98-08c2-44e9-f78b-09776dd0fc6f"
-      },
-      "source": [
-        "print(X_test.shape), print(ytest.shape)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "(99, 1)\n",
-            "(99,)\n"
-          ],
-          "name": "stdout"
-        },
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "(None, None)"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 12
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "fJ-XaSzikOqB"
-      },
-      "source": [
-        "# reshape input to be [samples, time steps, features] which is required for LSTM\n",
-        "X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)\n",
-        "X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "NVGFeeABkYCl"
-      },
-      "source": [
-        "model=keras.Sequential()\n",
-        "model.add(LSTM(500,return_sequences=True,input_shape=(X_train.shape[1], X_train.shape[2])))\n",
-        "model.add(LSTM(500,return_sequences=True))\n",
-        "model.add(LSTM(500,return_sequences=False))\n",
-        "model.add(Dropout(0.2))\n",
-        "model.add(Activation('sigmoid'))\n",
-        "model.add(Dense(1))\n",
-        "#opt = keras.optimizers.Adam(learning_rate=0.01)\n",
-        "#model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['mse'])\n",
-        "model.compile(loss='mean_squared_error',optimizer='adam', metrics=['mae'])"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "xF8-LcpsqMt6",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "5ef342e9-a93f-4a70-9316-72d3a0938303"
-      },
-      "source": [
-        "model.summary()"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Model: \"sequential\"\n",
-            "_________________________________________________________________\n",
-            "Layer (type)                 Output Shape              Param #   \n",
-            "=================================================================\n",
-            "lstm (LSTM)                  (None, 1, 500)            1004000   \n",
-            "_________________________________________________________________\n",
-            "lstm_1 (LSTM)                (None, 1, 500)            2002000   \n",
-            "_________________________________________________________________\n",
-            "lstm_2 (LSTM)                (None, 500)               2002000   \n",
-            "_________________________________________________________________\n",
-            "dropout (Dropout)            (None, 500)               0         \n",
-            "_________________________________________________________________\n",
-            "activation (Activation)      (None, 500)               0         \n",
-            "_________________________________________________________________\n",
-            "dense (Dense)                (None, 1)                 501       \n",
-            "=================================================================\n",
-            "Total params: 5,008,501\n",
-            "Trainable params: 5,008,501\n",
-            "Non-trainable params: 0\n",
-            "_________________________________________________________________\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "oLH_xT7qqdpT",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "446bc1f0-ddc3-4a52-c174-15ea63bf63a8"
-      },
-      "source": [
-        "history=model.fit(X_train,y_train,validation_data=(X_test,ytest),epochs=20,batch_size=32,verbose=1)"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Epoch 1/20\n",
-            "13/13 [==============================] - 6s 162ms/step - loss: 0.0422 - mae: 0.1598 - val_loss: 0.3443 - val_mae: 0.5704\n",
-            "Epoch 2/20\n",
-            "13/13 [==============================] - 1s 42ms/step - loss: 0.0216 - mae: 0.1215 - val_loss: 0.4032 - val_mae: 0.6202\n",
-            "Epoch 3/20\n",
-            "13/13 [==============================] - 1s 42ms/step - loss: 0.0168 - mae: 0.0962 - val_loss: 0.3642 - val_mae: 0.5901\n",
-            "Epoch 4/20\n",
-            "13/13 [==============================] - 1s 42ms/step - loss: 0.0096 - mae: 0.0663 - val_loss: 0.1141 - val_mae: 0.3296\n",
-            "Epoch 5/20\n",
-            "13/13 [==============================] - 1s 41ms/step - loss: 0.0023 - mae: 0.0315 - val_loss: 0.0232 - val_mae: 0.1477\n",
-            "Epoch 6/20\n",
-            "13/13 [==============================] - 1s 43ms/step - loss: 0.0011 - mae: 0.0186 - val_loss: 0.0129 - val_mae: 0.1058\n",
-            "Epoch 7/20\n",
-            "13/13 [==============================] - 1s 44ms/step - loss: 3.7233e-04 - mae: 0.0123 - val_loss: 0.0049 - val_mae: 0.0604\n",
-            "Epoch 8/20\n",
-            "13/13 [==============================] - 1s 45ms/step - loss: 1.6180e-04 - mae: 0.0084 - val_loss: 0.0041 - val_mae: 0.0538\n",
-            "Epoch 9/20\n",
-            "13/13 [==============================] - 1s 43ms/step - loss: 1.6337e-04 - mae: 0.0091 - val_loss: 0.0064 - val_mae: 0.0707\n",
-            "Epoch 10/20\n",
-            "13/13 [==============================] - 1s 45ms/step - loss: 1.9074e-04 - mae: 0.0077 - val_loss: 0.0035 - val_mae: 0.0487\n",
-            "Epoch 11/20\n",
-            "13/13 [==============================] - 1s 41ms/step - loss: 1.1686e-04 - mae: 0.0067 - val_loss: 0.0044 - val_mae: 0.0561\n",
-            "Epoch 12/20\n",
-            "13/13 [==============================] - 1s 41ms/step - loss: 1.7812e-04 - mae: 0.0083 - val_loss: 0.0033 - val_mae: 0.0474\n",
-            "Epoch 13/20\n",
-            "13/13 [==============================] - 1s 42ms/step - loss: 1.6930e-04 - mae: 0.0077 - val_loss: 0.0055 - val_mae: 0.0643\n",
-            "Epoch 14/20\n",
-            "13/13 [==============================] - 1s 42ms/step - loss: 1.1692e-04 - mae: 0.0069 - val_loss: 0.0028 - val_mae: 0.0425\n",
-            "Epoch 15/20\n",
-            "13/13 [==============================] - 1s 42ms/step - loss: 1.2750e-04 - mae: 0.0074 - val_loss: 0.0043 - val_mae: 0.0553\n",
-            "Epoch 16/20\n",
-            "13/13 [==============================] - 1s 41ms/step - loss: 1.9787e-04 - mae: 0.0098 - val_loss: 0.0059 - val_mae: 0.0675\n",
-            "Epoch 17/20\n",
-            "13/13 [==============================] - 1s 41ms/step - loss: 2.1901e-04 - mae: 0.0115 - val_loss: 0.0030 - val_mae: 0.0441\n",
-            "Epoch 18/20\n",
-            "13/13 [==============================] - 1s 42ms/step - loss: 1.0347e-04 - mae: 0.0061 - val_loss: 0.0027 - val_mae: 0.0419\n",
-            "Epoch 19/20\n",
-            "13/13 [==============================] - 1s 41ms/step - loss: 1.2707e-04 - mae: 0.0068 - val_loss: 0.0032 - val_mae: 0.0466\n",
-            "Epoch 20/20\n",
-            "13/13 [==============================] - 1s 42ms/step - loss: 1.6478e-04 - mae: 0.0088 - val_loss: 0.0037 - val_mae: 0.0503\n"
-          ],
-          "name": "stdout"
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "2-RCPm8wjBUb",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "bdbb3f64-e100-4436-d95d-169bc0e13d50"
-      },
-      "source": [
-        "history.history['loss']"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": [
-              "[0.03191959112882614,\n",
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-              " 0.0002838505315594375,\n",
-              " 0.00020431107259355485,\n",
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-              " 0.00015689247811678797,\n",
-              " 0.00014251841639634222,\n",
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-              " 0.000160191222676076,\n",
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-              " 0.000203924355446361,\n",
-              " 0.00019159115618094802,\n",
-              " 0.0001174519638880156,\n",
-              " 0.00013711779320146888,\n",
-              " 0.00019837942090816796]"
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-          "execution_count": 17
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-        "trace1 = go.Scatter(\n",
-        "    x = np.arange(0, len(history.history['loss']), 1),\n",
-        "    y = history.history['loss'],\n",
-        "    mode = 'lines',\n",
-        "    name = 'Train loss',\n",
-        "    line = dict(color=('rgb(255,0,0)'), width=2, dash='dash')\n",
-        ")\n",
-        "trace2 = go.Scatter(\n",
-        "    x = np.arange(0, len(history.history['val_loss']), 1),\n",
-        "    y = history.history['val_loss'],\n",
-        "    mode = 'lines',\n",
-        "    name = 'Test loss',\n",
-        "    line = dict(color=('rgb(1, 0, 255)'), width=2)\n",
-        ")\n",
-        "\n",
-        "data = [trace1, trace2]\n",
-        "layout = go.Layout(\n",
-        "    autosize=False,\n",
-        "    width=750,\n",
-        "    height=600,\n",
-        "\n",
-        "    title = 'Train and Test Loss during training',\n",
-        "    xaxis= go.layout.XAxis(linecolor = 'black',\n",
-        "                          linewidth = 1,\n",
-        "                          mirror = True,\n",
-        "                          title = \"Epoch number\",\n",
-        "                          color = 'black'\n",
-        "                          ),\n",
-        "\n",
-        "    yaxis= go.layout.YAxis(linecolor = 'red',\n",
-        "                          linewidth = 1,\n",
-        "                          mirror = True,\n",
-        "                          title = \"Loss\",\n",
-        "                           color = 'black'),\n",
-        "\n",
-        "    margin=go.layout.Margin(\n",
-        "        l=50,\n",
-        "        r=50,\n",
-        "        b=100,\n",
-        "        t=100,\n",
-        "        pad = 4\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "fig = go.Figure(data=data, layout=layout)\n",
-        "py.iplot(fig, filename='training_process')"
-      ],
-      "execution_count": null,
-      "outputs": [
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-              "        if (!display || display === 'none') {{\n",
-              "            console.log([gd, 'removed!']);\n",
-              "            Plotly.purge(gd);\n",
-              "            observer.disconnect();\n",
-              "        }}\n",
-              "}});\n",
-              "\n",
-              "// Listen for the removal of the full notebook cells\n",
-              "var notebookContainer = gd.closest('#notebook-container');\n",
-              "if (notebookContainer) {{\n",
-              "    x.observe(notebookContainer, {childList: true});\n",
-              "}}\n",
-              "\n",
-              "// Listen for the clearing of the current output cell\n",
-              "var outputEl = gd.closest('.output');\n",
-              "if (outputEl) {{\n",
-              "    x.observe(outputEl, {childList: true});\n",
-              "}}\n",
-              "\n",
-              "                        })\n",
-              "                };\n",
-              "                \n",
-              "            </script>\n",
-              "        </div>\n",
-              "</body>\n",
-              "</html>"
-            ]
-          },
-          "metadata": {
-            "tags": []
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-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "wt88cq4-CE4C",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 616
-        },
-        "outputId": "bc8520e0-71b8-42f3-9d7e-f9f340911ba2"
-      },
-      "source": [
-        "trace1 = go.Scatter(\n",
-        "    x = np.arange(0, len(history.history['mae']), 1),\n",
-        "    y = history.history['mae'],\n",
-        "    mode = 'lines',\n",
-        "    name = 'Train Mean Absolute Error',\n",
-        "    line = dict(color=('rgb(0,0,139)'), width=2, dash='dash')\n",
-        ")\n",
-        "trace2 = go.Scatter(\n",
-        "    x = np.arange(0, len(history.history['val_mae']), 1),\n",
-        "    y = history.history['val_mae'],\n",
-        "    mode = 'lines',\n",
-        "    name = 'Test Mean Absolute Error',\n",
-        "    line = dict(color=('rgb(1, 0, 0)'), width=2)\n",
-        ")\n",
-        "\n",
-        "data = [trace1, trace2]\n",
-        "layout = go.Layout(\n",
-        "    autosize=False,\n",
-        "    width=750,\n",
-        "    height=600,\n",
-        "\n",
-        "    title = 'Train and Test Mean Squared Error during training',\n",
-        "    xaxis= go.layout.XAxis(linecolor = 'black',\n",
-        "                          linewidth = 1,\n",
-        "                          mirror = True,\n",
-        "                          title = \"Epoch number\",\n",
-        "                          color = 'black'\n",
-        "                          ),\n",
-        "\n",
-        "    yaxis= go.layout.YAxis(linecolor = 'red',\n",
-        "                          linewidth = 1,\n",
-        "                          mirror = True,\n",
-        "                          title = \"Mean Absolute Error\",\n",
-        "                           color = 'black'),\n",
-        "\n",
-        "    margin=go.layout.Margin(\n",
-        "        l=50,\n",
-        "        r=50,\n",
-        "        b=100,\n",
-        "        t=100,\n",
-        "        pad = 4\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "fig = go.Figure(data=data, layout=layout)\n",
-        "py.iplot(fig, filename='training_process')"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
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-              "<head><meta charset=\"utf-8\" /></head>\n",
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\"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Train and Test Mean Squared Error during training\"}, \"width\": 750, \"xaxis\": {\"color\": \"black\", \"linecolor\": \"black\", \"linewidth\": 1, \"mirror\": true, \"title\": {\"text\": \"Epoch number\"}}, \"yaxis\": {\"color\": \"black\", \"linecolor\": \"red\", \"linewidth\": 1, \"mirror\": true, \"title\": {\"text\": \"Mean Absolute Error\"}}},\n",
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-              "            Plotly.purge(gd);\n",
-              "            observer.disconnect();\n",
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-              "\n",
-              "// Listen for the removal of the full notebook cells\n",
-              "var notebookContainer = gd.closest('#notebook-container');\n",
-              "if (notebookContainer) {{\n",
-              "    x.observe(notebookContainer, {childList: true});\n",
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-              "\n",
-              "// Listen for the clearing of the current output cell\n",
-              "var outputEl = gd.closest('.output');\n",
-              "if (outputEl) {{\n",
-              "    x.observe(outputEl, {childList: true});\n",
-              "}}\n",
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-              "                        })\n",
-              "                };\n",
-              "                \n",
-              "            </script>\n",
-              "        </div>\n",
-              "</body>\n",
-              "</html>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "jgbBKA6NClxf"
-      },
-      "source": [
-        "# add one additional data point to align shapes of the predictions and true labels\n",
-        "#X_test = np.append(X_test, scaler.transform(data.iloc[-1][0]))\n",
-        "#X_test = np.reshape(X_test, (len(X_test), 1, 1))\n",
-        "\n",
-        "# get predictions and then make some transformations to be able to calculate RMSE properly in USD\n",
-        "prediction = model.predict(X_test)\n",
-        "prediction_inverse = scaler.inverse_transform(prediction.reshape(-1, 1))\n",
-        "Y_test_inverse = scaler.inverse_transform(ytest.reshape(-1, 1))\n",
-        "prediction2_inverse = np.array(prediction_inverse[:,0][1:])\n",
-        "Y_test2_inverse = np.array(Y_test_inverse[:,0])"
-      ],
-      "execution_count": null,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "m8SXDEzbDPzM",
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "outputId": "f8613de7-48ba-4dc6-d1b1-618cca8b5402"
-      },
-      "source": [
-        "trace1 = go.Scatter(\n",
-        "    x = np.arange(0, len(prediction2_inverse), 1),\n",
-        "    y = prediction2_inverse,\n",
-        "    mode = 'lines',\n",
-        "    name = 'Predicted Labels',\n",
-        "    line = dict(color=('rgb(0,0,139)'), width=2)\n",
-        ")\n",
-        "trace2 = go.Scatter(\n",
-        "    x = np.arange(0, len(Y_test2_inverse), 1),\n",
-        "    y = Y_test2_inverse,\n",
-        "    mode = 'lines',\n",
-        "    name = 'True labels',\n",
-        "    line = dict(color=('rgb(255, 0, 0)'), width=2)\n",
-        "\n",
-        ")\n",
-        "data = [trace1, trace2]\n",
-        "\n",
-        "layout = go.Layout(\n",
-        "    autosize=False,\n",
-        "    width=750,\n",
-        "    height=600,\n",
-        "\n",
-        "    title = 'Prediction Graph',\n",
-        "    xaxis= go.layout.XAxis(linecolor = 'black',\n",
-        "                          linewidth = 1,\n",
-        "                          mirror = True,\n",
-        "                          title = \"Number of days\",\n",
-        "                          color = 'black'\n",
-        "                          ),                          \n",
-        "\n",
-        "    yaxis= go.layout.YAxis(linecolor = 'red',\n",
-        "                          linewidth = 1,\n",
-        "                          mirror = True,\n",
-        "                          title = \"Price in USD\",\n",
-        "                           color = 'black'),\n",
-        "\n",
-        "    margin=go.layout.Margin(\n",
-        "        l=50,\n",
-        "        r=50,\n",
-        "        b=100,\n",
-        "        t=100,\n",
-        "        pad = 4\n",
-        "    )\n",
-        ")\n",
-        "\n",
-        "fig = go.Figure(data=data, layout=layout)\n",
-        "py.iplot(fig, filename='results_demonstrating0')\n",
-        "plt.savefig('demonstrating0.png', dpi=300, transparent=False, bbox_inches='tight')\n"
-      ],
-      "execution_count": null,
-      "outputs": [
-        {
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\"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Prediction Graph\"}, \"width\": 750, \"xaxis\": {\"color\": \"black\", \"linecolor\": \"black\", \"linewidth\": 1, \"mirror\": true, \"title\": {\"text\": \"Number of days\"}}, \"yaxis\": {\"color\": \"black\", \"linecolor\": \"red\", \"linewidth\": 1, \"mirror\": true, \"title\": {\"text\": \"Price in USD\"}}},\n",
-              "                        {\"responsive\": true}\n",
-              "                    ).then(function(){\n",
-              "                            \n",
-              "var gd = document.getElementById('a79c7a85-6d21-421d-a360-a708d2b9e71f');\n",
-              "var x = new MutationObserver(function (mutations, observer) {{\n",
-              "        var display = window.getComputedStyle(gd).display;\n",
-              "        if (!display || display === 'none') {{\n",
-              "            console.log([gd, 'removed!']);\n",
-              "            Plotly.purge(gd);\n",
-              "            observer.disconnect();\n",
-              "        }}\n",
-              "}});\n",
-              "\n",
-              "// Listen for the removal of the full notebook cells\n",
-              "var notebookContainer = gd.closest('#notebook-container');\n",
-              "if (notebookContainer) {{\n",
-              "    x.observe(notebookContainer, {childList: true});\n",
-              "}}\n",
-              "\n",
-              "// Listen for the clearing of the current output cell\n",
-              "var outputEl = gd.closest('.output');\n",
-              "if (outputEl) {{\n",
-              "    x.observe(outputEl, {childList: true});\n",
-              "}}\n",
-              "\n",
-              "                        })\n",
-              "                };\n",
-              "                \n",
-              "            </script>\n",
-              "        </div>\n",
-              "</body>\n",
-              "</html>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": [
-              "<Figure size 720x432 with 0 Axes>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
-    }
-  ]
-}
\ No newline at end of file

From f78413e22e2c6e1043ce55485d1b4a384548c4da Mon Sep 17 00:00:00 2001
From: Bilal Ahmad <50988601+billypentester@users.noreply.github.com>
Date: Fri, 22 Oct 2021 17:57:25 +0500
Subject: [PATCH 065/232] create alphabhet_short.js

---
 Javascript/short_alpha.js | 4 ++++
 1 file changed, 4 insertions(+)
 create mode 100644 Javascript/short_alpha.js

diff --git a/Javascript/short_alpha.js b/Javascript/short_alpha.js
new file mode 100644
index 0000000..3151fde
--- /dev/null
+++ b/Javascript/short_alpha.js
@@ -0,0 +1,4 @@
+const sortCharactersInString = str =>
+  [...str].sort((a, b) => a.localeCompare(b)).join('');
+ 
+console.log(sortCharactersInString('cabbage'));

From 970fce93c7445e44b9965f99e204bb972b6bad1d Mon Sep 17 00:00:00 2001
From: Bilal Ahmad <50988601+billypentester@users.noreply.github.com>
Date: Fri, 22 Oct 2021 17:59:03 +0500
Subject: [PATCH 066/232] count vowel.js

---
 Javascript/count.js | 7 +++++++
 1 file changed, 7 insertions(+)
 create mode 100644 Javascript/count.js

diff --git a/Javascript/count.js b/Javascript/count.js
new file mode 100644
index 0000000..c90ddb0
--- /dev/null
+++ b/Javascript/count.js
@@ -0,0 +1,7 @@
+function vowel_count(str) {
+
+  return str.match(/[aeiou]/gi).length;
+
+}
+
+console.log(vowel_count('The quick brown fox jumps over the lazy dog'));

From 14e6b521fbc1427011e5d0f373045c943985a05f Mon Sep 17 00:00:00 2001
From: Kainat-Mahar <72600530+Kainat-Mahar@users.noreply.github.com>
Date: Fri, 22 Oct 2021 19:15:10 +0500
Subject: [PATCH 067/232] Add files via upload

---
 ...cation_using_Random_Forest_Algorithm.ipynb | 1355 +++++++++++++++++
 1 file changed, 1355 insertions(+)
 create mode 100644 Data Science/Classification_using_Random_Forest_Algorithm.ipynb

diff --git a/Data Science/Classification_using_Random_Forest_Algorithm.ipynb b/Data Science/Classification_using_Random_Forest_Algorithm.ipynb
new file mode 100644
index 0000000..ef5e38c
--- /dev/null
+++ b/Data Science/Classification_using_Random_Forest_Algorithm.ipynb	
@@ -0,0 +1,1355 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Classification using Random Forest Algorithm.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HbbGwEA47kDj"
+      },
+      "source": [
+        "Importing Libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "roYk-Q3s6yVd"
+      },
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib as plt\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "execution_count": 1,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7v8I08N58jcA"
+      },
+      "source": [
+        "Data Wrangling"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ttq0PCrZ8i_d",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 202
+        },
+        "outputId": "d2a6d459-e354-475f-d2c5-ef08982f82d0"
+      },
+      "source": [
+        "data = pd.read_csv('dataset.csv')\n",
+        "\n",
+        "data.head(5)"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>cp</th>\n",
+              "      <th>trestbps</th>\n",
+              "      <th>chol</th>\n",
+              "      <th>fbs</th>\n",
+              "      <th>restecg</th>\n",
+              "      <th>thalach</th>\n",
+              "      <th>exang</th>\n",
+              "      <th>oldpeak</th>\n",
+              "      <th>slope</th>\n",
+              "      <th>ca</th>\n",
+              "      <th>thal</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>63</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>145</td>\n",
+              "      <td>233</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>150</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>37</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>130</td>\n",
+              "      <td>250</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>187</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3.5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>41</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>130</td>\n",
+              "      <td>204</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>172</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1.4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>56</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>120</td>\n",
+              "      <td>236</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>178</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>57</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>120</td>\n",
+              "      <td>354</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>163</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.6</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
+              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
+              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
+              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
+              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
+              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
+              "\n",
+              "[5 rows x 14 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8PJwi9rc8tp-",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4e8f191b-6599-496e-b97b-8fcbedbc7b22"
+      },
+      "source": [
+        "print(\"(Rows, columns): \" + str(data.shape))\n",
+        "data.columns "
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(Rows, columns): (303, 14)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
+              "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PxY79ivL80gB",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "73b9d73d-e643-4602-ac52-c8df5d5a8413"
+      },
+      "source": [
+        "data.nunique(axis=0)# returns the number of unique values for each variable."
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "age          41\n",
+              "sex           2\n",
+              "cp            4\n",
+              "trestbps     49\n",
+              "chol        152\n",
+              "fbs           2\n",
+              "restecg       3\n",
+              "thalach      91\n",
+              "exang         2\n",
+              "oldpeak      40\n",
+              "slope         3\n",
+              "ca            5\n",
+              "thal          4\n",
+              "target        2\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wn63NFgG88dd",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 477
+        },
+        "outputId": "83fe2164-71d8-438a-f38f-6076eb0512c6"
+      },
+      "source": [
+        "#summarizes the count, mean, standard deviation, min, and max for numeric variables.\n",
+        "data.describe().transpose()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>min</th>\n",
+              "      <th>25%</th>\n",
+              "      <th>50%</th>\n",
+              "      <th>75%</th>\n",
+              "      <th>max</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>age</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>54.366337</td>\n",
+              "      <td>9.082101</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>47.5</td>\n",
+              "      <td>55.0</td>\n",
+              "      <td>61.0</td>\n",
+              "      <td>77.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sex</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.683168</td>\n",
+              "      <td>0.466011</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>cp</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.966997</td>\n",
+              "      <td>1.032052</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>trestbps</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>131.623762</td>\n",
+              "      <td>17.538143</td>\n",
+              "      <td>94.0</td>\n",
+              "      <td>120.0</td>\n",
+              "      <td>130.0</td>\n",
+              "      <td>140.0</td>\n",
+              "      <td>200.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>chol</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>246.264026</td>\n",
+              "      <td>51.830751</td>\n",
+              "      <td>126.0</td>\n",
+              "      <td>211.0</td>\n",
+              "      <td>240.0</td>\n",
+              "      <td>274.5</td>\n",
+              "      <td>564.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>fbs</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.148515</td>\n",
+              "      <td>0.356198</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>restecg</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.528053</td>\n",
+              "      <td>0.525860</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>thalach</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>149.646865</td>\n",
+              "      <td>22.905161</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>133.5</td>\n",
+              "      <td>153.0</td>\n",
+              "      <td>166.0</td>\n",
+              "      <td>202.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>exang</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.326733</td>\n",
+              "      <td>0.469794</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>oldpeak</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>1.039604</td>\n",
+              "      <td>1.161075</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.8</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>6.2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>slope</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>1.399340</td>\n",
+              "      <td>0.616226</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>ca</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.729373</td>\n",
+              "      <td>1.022606</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>4.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>thal</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>2.313531</td>\n",
+              "      <td>0.612277</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>target</th>\n",
+              "      <td>303.0</td>\n",
+              "      <td>0.544554</td>\n",
+              "      <td>0.498835</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "          count        mean        std    min    25%    50%    75%    max\n",
+              "age       303.0   54.366337   9.082101   29.0   47.5   55.0   61.0   77.0\n",
+              "sex       303.0    0.683168   0.466011    0.0    0.0    1.0    1.0    1.0\n",
+              "cp        303.0    0.966997   1.032052    0.0    0.0    1.0    2.0    3.0\n",
+              "trestbps  303.0  131.623762  17.538143   94.0  120.0  130.0  140.0  200.0\n",
+              "chol      303.0  246.264026  51.830751  126.0  211.0  240.0  274.5  564.0\n",
+              "fbs       303.0    0.148515   0.356198    0.0    0.0    0.0    0.0    1.0\n",
+              "restecg   303.0    0.528053   0.525860    0.0    0.0    1.0    1.0    2.0\n",
+              "thalach   303.0  149.646865  22.905161   71.0  133.5  153.0  166.0  202.0\n",
+              "exang     303.0    0.326733   0.469794    0.0    0.0    0.0    1.0    1.0\n",
+              "oldpeak   303.0    1.039604   1.161075    0.0    0.0    0.8    1.6    6.2\n",
+              "slope     303.0    1.399340   0.616226    0.0    1.0    1.0    2.0    2.0\n",
+              "ca        303.0    0.729373   1.022606    0.0    0.0    0.0    1.0    4.0\n",
+              "thal      303.0    2.313531   0.612277    0.0    2.0    2.0    3.0    3.0\n",
+              "target    303.0    0.544554   0.498835    0.0    0.0    1.0    1.0    1.0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "a6ushlSP9EJP",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "366a429a-9d57-4ea8-bc8d-b51b0baf45fc"
+      },
+      "source": [
+        "# Display the Missing Values\n",
+        "\n",
+        "print(data.isna().sum())"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "age         0\n",
+            "sex         0\n",
+            "cp          0\n",
+            "trestbps    0\n",
+            "chol        0\n",
+            "fbs         0\n",
+            "restecg     0\n",
+            "thalach     0\n",
+            "exang       0\n",
+            "oldpeak     0\n",
+            "slope       0\n",
+            "ca          0\n",
+            "thal        0\n",
+            "target      0\n",
+            "dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IXOMCsIl9JpJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 612
+        },
+        "outputId": "7b01ed95-db2a-435c-f6e5-c8423976082c"
+      },
+      "source": [
+        "# calculate correlation matrix\n",
+        "\n",
+        "corr = data.corr()\n",
+        "plt.subplots(figsize=(15,10))\n",
+        "sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns, annot=True, cmap=sns.diverging_palette(220, 20, as_cmap=True))\n",
+        "sns.heatmap(corr, xticklabels=corr.columns,\n",
+        "            yticklabels=corr.columns, \n",
+        "            annot=True,\n",
+        "            cmap=sns.diverging_palette(220, 20, as_cmap=True))"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c953e9d10>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1080x720 with 3 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qqZ2RHNI9gFK"
+      },
+      "source": [
+        "Filtering data by positive & negative Heart Disease patient"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "n9lROx7X9T2W",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 314
+        },
+        "outputId": "ca94464a-e45e-4429-d316-efebe625f5d0"
+      },
+      "source": [
+        "# Filtering data by POSITIVE Heart Disease patient\n",
+        "pos_data = data[data['target']==1]\n",
+        "pos_data.describe()"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>cp</th>\n",
+              "      <th>trestbps</th>\n",
+              "      <th>chol</th>\n",
+              "      <th>fbs</th>\n",
+              "      <th>restecg</th>\n",
+              "      <th>thalach</th>\n",
+              "      <th>exang</th>\n",
+              "      <th>oldpeak</th>\n",
+              "      <th>slope</th>\n",
+              "      <th>ca</th>\n",
+              "      <th>thal</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.000000</td>\n",
+              "      <td>165.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>52.496970</td>\n",
+              "      <td>0.563636</td>\n",
+              "      <td>1.375758</td>\n",
+              "      <td>129.303030</td>\n",
+              "      <td>242.230303</td>\n",
+              "      <td>0.139394</td>\n",
+              "      <td>0.593939</td>\n",
+              "      <td>158.466667</td>\n",
+              "      <td>0.139394</td>\n",
+              "      <td>0.583030</td>\n",
+              "      <td>1.593939</td>\n",
+              "      <td>0.363636</td>\n",
+              "      <td>2.121212</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>9.550651</td>\n",
+              "      <td>0.497444</td>\n",
+              "      <td>0.952222</td>\n",
+              "      <td>16.169613</td>\n",
+              "      <td>53.552872</td>\n",
+              "      <td>0.347412</td>\n",
+              "      <td>0.504818</td>\n",
+              "      <td>19.174276</td>\n",
+              "      <td>0.347412</td>\n",
+              "      <td>0.780683</td>\n",
+              "      <td>0.593635</td>\n",
+              "      <td>0.848894</td>\n",
+              "      <td>0.465752</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>29.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>94.000000</td>\n",
+              "      <td>126.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>96.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>44.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>120.000000</td>\n",
+              "      <td>208.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>149.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>52.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>130.000000</td>\n",
+              "      <td>234.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>161.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.200000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>59.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>140.000000</td>\n",
+              "      <td>267.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>172.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>76.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>180.000000</td>\n",
+              "      <td>564.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>202.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>4.200000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>4.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              age         sex          cp  ...          ca        thal  target\n",
+              "count  165.000000  165.000000  165.000000  ...  165.000000  165.000000   165.0\n",
+              "mean    52.496970    0.563636    1.375758  ...    0.363636    2.121212     1.0\n",
+              "std      9.550651    0.497444    0.952222  ...    0.848894    0.465752     0.0\n",
+              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000     1.0\n",
+              "25%     44.000000    0.000000    1.000000  ...    0.000000    2.000000     1.0\n",
+              "50%     52.000000    1.000000    2.000000  ...    0.000000    2.000000     1.0\n",
+              "75%     59.000000    1.000000    2.000000  ...    0.000000    2.000000     1.0\n",
+              "max     76.000000    1.000000    3.000000  ...    4.000000    3.000000     1.0\n",
+              "\n",
+              "[8 rows x 14 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iNdi8cwN9wmU",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 314
+        },
+        "outputId": "1e819b7f-f8cc-4418-f4f7-d5c8c010c77e"
+      },
+      "source": [
+        "# Filtering data by NEGATIVE Heart Disease patient\n",
+        "pos_data = data[data['target']==0]\n",
+        "pos_data.describe()"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>age</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>cp</th>\n",
+              "      <th>trestbps</th>\n",
+              "      <th>chol</th>\n",
+              "      <th>fbs</th>\n",
+              "      <th>restecg</th>\n",
+              "      <th>thalach</th>\n",
+              "      <th>exang</th>\n",
+              "      <th>oldpeak</th>\n",
+              "      <th>slope</th>\n",
+              "      <th>ca</th>\n",
+              "      <th>thal</th>\n",
+              "      <th>target</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.000000</td>\n",
+              "      <td>138.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>56.601449</td>\n",
+              "      <td>0.826087</td>\n",
+              "      <td>0.478261</td>\n",
+              "      <td>134.398551</td>\n",
+              "      <td>251.086957</td>\n",
+              "      <td>0.159420</td>\n",
+              "      <td>0.449275</td>\n",
+              "      <td>139.101449</td>\n",
+              "      <td>0.550725</td>\n",
+              "      <td>1.585507</td>\n",
+              "      <td>1.166667</td>\n",
+              "      <td>1.166667</td>\n",
+              "      <td>2.543478</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>7.962082</td>\n",
+              "      <td>0.380416</td>\n",
+              "      <td>0.905920</td>\n",
+              "      <td>18.729944</td>\n",
+              "      <td>49.454614</td>\n",
+              "      <td>0.367401</td>\n",
+              "      <td>0.541321</td>\n",
+              "      <td>22.598782</td>\n",
+              "      <td>0.499232</td>\n",
+              "      <td>1.300340</td>\n",
+              "      <td>0.561324</td>\n",
+              "      <td>1.043460</td>\n",
+              "      <td>0.684762</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>35.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>131.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>71.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>52.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>120.000000</td>\n",
+              "      <td>217.250000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>125.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.600000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>58.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>130.000000</td>\n",
+              "      <td>249.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>142.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.400000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>62.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>144.750000</td>\n",
+              "      <td>283.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>156.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.500000</td>\n",
+              "      <td>1.750000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>77.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>200.000000</td>\n",
+              "      <td>409.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>195.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>6.200000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>4.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "              age         sex          cp  ...          ca        thal  target\n",
+              "count  138.000000  138.000000  138.000000  ...  138.000000  138.000000   138.0\n",
+              "mean    56.601449    0.826087    0.478261  ...    1.166667    2.543478     0.0\n",
+              "std      7.962082    0.380416    0.905920  ...    1.043460    0.684762     0.0\n",
+              "min     35.000000    0.000000    0.000000  ...    0.000000    0.000000     0.0\n",
+              "25%     52.000000    1.000000    0.000000  ...    0.000000    2.000000     0.0\n",
+              "50%     58.000000    1.000000    0.000000  ...    1.000000    3.000000     0.0\n",
+              "75%     62.000000    1.000000    0.000000  ...    2.000000    3.000000     0.0\n",
+              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000     0.0\n",
+              "\n",
+              "[8 rows x 14 columns]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DbzJPYQV9lPu"
+      },
+      "source": [
+        "Assign the 13 features to X, & the last column to our classification predictor, y"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FjMSVs94960R"
+      },
+      "source": [
+        "X = data.iloc[:, :-1].values\n",
+        "y = data.iloc[:, -1].values"
+      ],
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kYwABWTb-AJx"
+      },
+      "source": [
+        "Split: the data set into the Training set and Test set"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "DlgSSiYE99tk"
+      },
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "x_train, x_test, y_train, y_test = train_test_split(X,y,test_size = 0.2, random_state = 1)"
+      ],
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MuoZklrK-FJY"
+      },
+      "source": [
+        "Normalize: Standardizing the data a mean of 0 and a standard deviation of 1."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rGfWqZir-LLs"
+      },
+      "source": [
+        "from sklearn.preprocessing import StandardScaler\n",
+        "sc = StandardScaler()\n",
+        "x_train = sc.fit_transform(x_train)\n",
+        "x_test = sc.transform(x_test)"
+      ],
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CwBqTSrI-WVv"
+      },
+      "source": [
+        "Modeling /Training"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xk0Uem6P-qgq"
+      },
+      "source": [
+        "Model: Random Forest\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "JaSaSKDT-qE-",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bbcc9de5-0a3e-4a70-9eb9-3530cc7803e0"
+      },
+      "source": [
+        "from sklearn.metrics import classification_report \n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "\n",
+        "model3 = RandomForestClassifier(random_state=1)# get instance of model\n",
+        "model3.fit(x_train, y_train) # Train/Fit model \n",
+        "\n",
+        "y_pred3 = model3.predict(x_test) # get y predictions\n",
+        "print(classification_report(y_test, y_pred3)) # output accuracy"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.88      0.70      0.78        30\n",
+            "           1       0.76      0.90      0.82        31\n",
+            "\n",
+            "    accuracy                           0.80        61\n",
+            "   macro avg       0.82      0.80      0.80        61\n",
+            "weighted avg       0.81      0.80      0.80        61\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NYkRl2Et-1vo"
+      },
+      "source": [
+        "Making the Confusion Matrix"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qH0hRD8j-3m9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f8f38c70-ce4c-4824-9ba3-13d9e6b06f5b"
+      },
+      "source": [
+        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+        "cm = confusion_matrix(y_test, y_pred3)\n",
+        "print(cm)\n",
+        "accuracy_score(y_test, y_pred3)"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[21  9]\n",
+            " [ 3 28]]\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.8032786885245902"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fu7KQOnjzY6g",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "87984799-bb19-4075-de5c-d83033940f4c"
+      },
+      "source": [
+        "TN = cm[0][0]\n",
+        "FN = cm[1][0]\n",
+        "TP = cm[1][1]\n",
+        "FP = cm[0][1]\n",
+        "\n",
+        "specificity = (TN/TN+FP)\n",
+        "\n",
+        "print ('Specificity = ', specificity)"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Specificity =  10.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Vt5yCe496RoC",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "082a4a24-d607-44b9-d067-f69f77657a84"
+      },
+      "source": [
+        "senstivity = (TP/TP+FN)\n",
+        "\n",
+        "print ('Senstivity = ', senstivity)"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Senstivity =  4.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HrNkVFg8-_hj"
+      },
+      "source": [
+        "Feature Importance"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qCRNG8uR-6mX",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9cbe2af3-6685-4e5e-9c62-6124190834ae"
+      },
+      "source": [
+        "# get importance\n",
+        "importance = model3.feature_importances_\n",
+        "\n",
+        "# summarize feature importance\n",
+        "for i,v in enumerate(importance):\n",
+        "    print('Feature: %0d, Score: %.5f' % (i,v))"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Feature: 0, Score: 0.07814\n",
+            "Feature: 1, Score: 0.04206\n",
+            "Feature: 2, Score: 0.16580\n",
+            "Feature: 3, Score: 0.07477\n",
+            "Feature: 4, Score: 0.07587\n",
+            "Feature: 5, Score: 0.00828\n",
+            "Feature: 6, Score: 0.02014\n",
+            "Feature: 7, Score: 0.12772\n",
+            "Feature: 8, Score: 0.06950\n",
+            "Feature: 9, Score: 0.09957\n",
+            "Feature: 10, Score: 0.04677\n",
+            "Feature: 11, Score: 0.11667\n",
+            "Feature: 12, Score: 0.07473\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tUtHmfsu_EOF",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 282
+        },
+        "outputId": "3ccc0576-c4dd-41ba-e83e-cac8040ced12"
+      },
+      "source": [
+        "index= data.columns[:-1]\n",
+        "importance = pd.Series(model3.feature_importances_, index=index)\n",
+        "importance.nlargest(13).plot(kind='barh', colormap='winter')"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5c8d92dbd0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 51c267d259f85589550a9ab97f94a001ef90c216 Mon Sep 17 00:00:00 2001
From: Jagrit <69034224+jagritvats@users.noreply.github.com>
Date: Fri, 22 Oct 2021 21:14:39 +0530
Subject: [PATCH 068/232] Create insertionSort.cpp

---
 C++/insertionSort.cpp | 64 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 64 insertions(+)
 create mode 100644 C++/insertionSort.cpp

diff --git a/C++/insertionSort.cpp b/C++/insertionSort.cpp
new file mode 100644
index 0000000..dc9a565
--- /dev/null
+++ b/C++/insertionSort.cpp
@@ -0,0 +1,64 @@
+#include <iostream>
+using namespace std;
+
+// Method to print the elements of the array
+void displayArr(int a[], int n)
+{
+    for (int i = 0; i < n; i++)
+    {
+        cout << a[i] << " ";
+    }
+    cout << endl;
+}
+
+void insertionSort(int a[], int n)
+{
+    for (int i = 1; i < n; i++)
+    {
+        int j = i - 1;
+        int ele = a[i];
+        while (j >= 0 && a[j] > ele)
+        {
+            a[j + 1] = a[j];
+            j--;
+        }
+        a[j + 1] = ele;
+    }
+}
+
+int main()
+{
+    // Input the size and elements of array
+    int n;
+    cin >> n;
+    int a[n];
+    int x = 0;
+    while (x < n)
+    {
+        cin >> a[x];
+        x++;
+    }
+
+    int flag = 0;
+    for (int i = 0; i < n - 1; ++i)
+    {
+        if (a[i + 1] < a[i])
+        {
+            flag = 1;
+            break;
+        }
+    }
+
+    if (flag == 1)
+    {
+        cout << "\nSorted Array: ";
+        insertionSort(a, n);
+    }
+    else
+    {
+        cout << "\nData is already sorted: ";
+    }
+
+    // Display the Final Array
+    displayArr(a, n);
+}

From 6b46867e0e17176efeb79a511e5a4902258597da Mon Sep 17 00:00:00 2001
From: RizwanAmjad <rizwanamjadms@gmail.com>
Date: Sat, 23 Oct 2021 07:29:07 +0500
Subject: [PATCH 069/232] Added: Two new Algorithms Min Max Algorithm and
 Alfabeta pruning

---
 Python/AlfaBetaPruning.py | 159 ++++++++++++++++++++++++++++++++++++++
 Python/MinMax.py          | 131 +++++++++++++++++++++++++++++++
 2 files changed, 290 insertions(+)
 create mode 100644 Python/AlfaBetaPruning.py
 create mode 100644 Python/MinMax.py

diff --git a/Python/AlfaBetaPruning.py b/Python/AlfaBetaPruning.py
new file mode 100644
index 0000000..612f33d
--- /dev/null
+++ b/Python/AlfaBetaPruning.py
@@ -0,0 +1,159 @@
+import math
+
+
+class Problem:
+
+    def __init__(self):
+        # it a tree that is represented in the form of nested dictionary
+        # where every key represents a node having child and last nodes
+        # without child are represented in a list
+
+        """self.tree = {
+                    'A': {
+                        'B': {
+                            'D':
+                                [3, 8]
+                            ,
+                            'E':
+                                [6, 4]
+
+                        },
+                        'C': {
+                            'F':
+                                [2, 3]
+                            ,
+                            'G':
+                                [1, 2]
+
+                        }
+                    }
+                }"""
+        # Second symmetrical tree with each node having 3 children
+        '''self.tree = {
+            'A': {
+                'B': {
+                    'E':[3, 8, 4]
+                    ,
+                    'F':[6, 4, 1]
+                    ,
+                    'G':[1, 2,-1]
+
+
+                },
+                'C': {
+                    'H':[2, 3, 5]
+                    ,
+                    'I':[1, 2, 0]
+                    ,
+                    'J':[8, 2, 4]
+
+
+                },
+                'D': {
+                    'K':[2, 3, 5]
+                    ,
+                    'L':[1, 2, 7]
+                    ,
+                    'M':[5, 4, 9]
+
+
+                }
+            }
+        }'''
+        # third unsymmetrical tree with different number of children
+        # that looks like this
+        #         A
+        #       /    \
+        #      B         C
+        #    /   \    /  |     \
+        #   D    E    F   G     H
+        #   |   /\   |  / \  / | \
+        #   5   3 6  9  2 0  9 8 4
+        self.tree = {
+            'A': {
+                'B': {
+                    'D': [5]
+                    ,
+                    'E': [3, 6]
+                },
+                'C': {
+                    'F': [9]
+                    ,
+                    'G': [2, 0]
+                    ,
+                    'H': [9, 8, 4]
+
+                }
+            }
+        }
+
+    def apply_alpha_beta_pruning(self, current_subtree=None, min_max_chose=max, alpha=-math.inf, beta=math.inf):
+        """
+        Recursive function that gets a tree stored as dictionary
+        and calls itself recursively to apply min max on the sub-tree
+        on each recursive call it gets back an integer value that tree should have on each node.
+        Each node is represented by a recursive scope
+
+        :param current_subtree: type dict
+        :param min_max_chose: is a function default value is max.
+        :param alpha: int value of alpha default -inf
+        :param beta: int value of alpha default +inf
+        :return: an int
+        """
+
+        if current_subtree is None:
+            current_subtree = self.tree
+            print('Alpha Beta Pruning Started')
+        # list that contains integers that are returned from next recursive scope.
+        # is a min or max of child nodes.
+        returned_values = list()
+        # min_max_chose stores a function that alternates in every scope (has min and max values only)
+        # in one scope it is min and in next scope it is max
+
+        # inverting min_max_chose because it will apply on child scope or nodes
+        min_max_chose = max if min_max_chose == min else min
+        # base condition where node does not have further children and is represented as a list
+        if type(current_subtree) == list:
+            values = list()
+            # iterating over the values of child nodes to update value of alpha and beta since this is last node
+            for value in current_subtree:
+                values.append(value)
+                if min_max_chose == max:
+                    alpha = max([alpha, value])
+                elif min_max_chose == min:
+                    beta = min([beta, value])
+                # print(f"Currently alpha={alpha}, beta={beta}")
+                # condition for pruning
+                if beta <= alpha:
+                    print(f"Pruned alpha {alpha}, beta {beta}")
+                    break
+
+            return min_max_chose(values)
+        # available_keys has values of all available keys i-e child node
+        available_keys = current_subtree.keys()
+        # we iterate over these keys to get child subtrees
+        for key in available_keys:
+            value = self.apply_alpha_beta_pruning(current_subtree[key], min_max_chose, alpha, beta)
+            returned_values.append(value)
+            if min_max_chose == max:
+                alpha = max([alpha, value])
+            elif min_max_chose == min:
+                beta = min([beta, value])
+            print(f"Currently alpha={alpha}, beta={beta}")
+            # condition for pruning
+            if beta <= alpha:
+                print(f"Pruned alpha {alpha}, beta {beta}")
+                break
+        print(f"Values {returned_values} are returned to {'max' if min_max_chose == min else 'min'} scope"
+              f"and alpha= {alpha}, beta={beta}")
+        # return min or max depending on which scope it is.
+        return min_max_chose(returned_values)
+
+
+def main():
+    p = Problem()
+    print(p.apply_alpha_beta_pruning())
+
+
+if __name__ == '__main__':
+    main()
diff --git a/Python/MinMax.py b/Python/MinMax.py
new file mode 100644
index 0000000..72801e5
--- /dev/null
+++ b/Python/MinMax.py
@@ -0,0 +1,131 @@
+class Problem:
+
+    def __init__(self):
+        # it a tree that is represented in the form of nested dictionary
+        # where every key represents a node having child and last nodes
+        # without child are represented in a list
+        # First symmetrical tree with each node having 2 children
+        """self.tree = {
+            'A': {
+                'B': {
+                    'D':
+                        [3, 8]
+                    ,
+                    'E':
+                        [6, 4]
+
+                },
+                'C': {
+                    'F':
+                        [2, 3]
+                    ,
+                    'G':
+                        [1, 2]
+
+                }
+            }
+        }"""
+        # Second symmetrical tree with each node having 3 children
+        '''self.tree = {
+            'A': {
+                'B': {
+                    'E':[3, 8, 4]
+                    ,
+                    'F':[6, 4, 1]
+                    ,
+                    'G':[1, 2,-1]
+
+
+                },
+                'C': {
+                    'H':[2, 3, 5]
+                    ,
+                    'I':[1, 2, 0]
+                    ,
+                    'J':[8, 2, 4]
+
+
+                },
+                'D': {
+                    'K':[2, 3, 5]
+                    ,
+                    'L':[1, 2, 7]
+                    ,
+                    'M':[5, 4, 9]
+
+
+                }
+            }
+        }'''
+        # third unsymmetrical tree with different number of children
+        # that looks like this
+        #         A
+        #       /    \
+        #      B         C
+        #    /   \    /  |     \
+        #   D    E    F   G     H
+        #   |   /\   |  / \  / | \
+        #   5   3 6  9  2 0  9 8 4
+        self.tree = {
+            'A': {
+                'B': {
+                    'D': [5]
+                    ,
+                    'E': [3, 6]
+                },
+                'C': {
+                    'F': [9]
+                    ,
+                    'G': [2, 0]
+                    ,
+                    'H': [9, 8, 4]
+
+                }
+            }
+        }
+
+    def apply_min_max(self, current_subtree=None, min_max_chose=max):
+        """
+        Recursive function that gets a tree stored as dictionary
+        and calls itself recursively to apply min max on the sub-tree
+        on each recursive call it gets back an integer value that tree should have on each node.
+        Each node is represented by a recursive scope
+
+        :param current_subtree: type dict
+        :param min_max_chose: is a function default value is max.
+        :return: an int
+        """
+        if current_subtree is None:
+            current_subtree = self.tree
+            print('Min Max Started')
+
+        # list that contains integers that are returned from next recursive scope.
+        # is a min or max of child nodes.
+        returned_values = list()
+        # min_max_chose stores a function that alternates in every scope (has min and max values only)
+        # in one scope it is min and in next scope it is max
+
+        # inverting min_max_chose because it will apply on child scope or nodes
+        min_max_chose = max if min_max_chose == min else min
+        # base condition where node does not have further children and is represented as a list
+        if type(current_subtree) == list:
+            return min_max_chose(current_subtree)
+        # available_keys has values of all available keys i-e child node
+        available_keys = current_subtree.keys()
+        # we iterate over these keys to get child subtrees
+        for key in available_keys:
+            value = self.apply_min_max(current_subtree[key], min_max_chose)
+            returned_values.append(value)
+
+        print(f"Values {returned_values} are returned to {'max' if min_max_chose == min else 'min'} scope")
+        # return min or max depending on which scope it is.
+        return min_max_chose(returned_values)
+
+
+def main():
+    p = Problem()
+    print(p.apply_min_max())
+
+
+if __name__ == '__main__':
+    main()

From 97a172be7e4bc239c7884d1a3d42f3baef249f3e Mon Sep 17 00:00:00 2001
From: Jagrit <69034224+jagritvats@users.noreply.github.com>
Date: Sat, 23 Oct 2021 12:47:47 +0530
Subject: [PATCH 070/232] Update insertionSort.cpp

---
 C++/insertionSort.cpp | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/C++/insertionSort.cpp b/C++/insertionSort.cpp
index dc9a565..cd0bd05 100644
--- a/C++/insertionSort.cpp
+++ b/C++/insertionSort.cpp
@@ -62,3 +62,21 @@ int main()
     // Display the Final Array
     displayArr(a, n);
 }
+
+/*
+    Insertion Sort Algorithm in C++
+    Input: Length of Array followed by Array of integers
+    Output: Sorted array in ascending order
+    
+    Sample Input 1:
+        5
+        65 23 123 5 212
+    Output 1:
+        Sorted Array: 5 23 65 123 212 
+    
+    Sample Input 2:
+        7
+        32 54 112 43 23 1 2
+    Output 2:
+        Sorted Array: 1 2 23 32 43 54 112
+*/

From aa2766041fe11d8fc9d9b5815272fede9710aea9 Mon Sep 17 00:00:00 2001
From: LaraibNoor <nlaraib355@gmail.com>
Date: Sat, 23 Oct 2021 12:44:14 +0500
Subject: [PATCH 071/232] Added GBFS python implementation

---
 GBFS.py | 211 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 211 insertions(+)
 create mode 100644 GBFS.py

diff --git a/GBFS.py b/GBFS.py
new file mode 100644
index 0000000..614d33c
--- /dev/null
+++ b/GBFS.py
@@ -0,0 +1,211 @@
+
+            #Laraib Noor
+
+           #FA18-BSE-038
+
+
+#_______________________________________________________________________________________________________________
+
+
+class Priority_Queue:
+
+    def __init__(self):
+        self.back = list()
+        self.count = 0
+
+    def enqueue(self, value):
+        self.back.append(value)
+        self.count += 1
+
+    def dequeue(self):
+        if self.count > 0:
+            self.count -= 1
+            min_index = 0
+            min_value = 0
+            for i in range(len(self.back)):
+                if min_value > self.back[i].f:
+                    min_value = self.back[i].f
+                    min_index = i
+            return self.back.pop(min_index)
+        else:
+            return None
+
+    def contains(self, value):
+        for val in self.back:
+            if val == value:
+                return True
+        return False
+
+    def to_string(self):
+        return str(self.back)
+
+
+# Priority Queue ends here
+
+class Problem(object):
+    def __init__(self, init_state, goal_state):
+        self.initial_state = init_state;
+        self.goal_state = goal_state;
+        self.state_space = {};
+        self.state_space['Arad'] = {'R1': 'Zerind', 'R2': 'Sibiu', 'R3': 'Timisoara'};
+        self.state_space['Zerind'] = {'R1': 'Oradea', 'R2': 'Arad'};
+        self.state_space['Oradea'] = {'R1': 'Sibiu', 'R2': 'Zerind'};
+        self.state_space['Timisoara'] = {'R1': 'Lugoj', 'R2': 'Arad'};
+        self.state_space['Lugoj'] = {'R1': 'Timisoara', 'R2': 'Mehandia'};
+        self.state_space['Drobeta'] = {'R1': 'Mehandia', 'R2': 'Craiova'};
+        self.state_space['Craiova'] = {'R1': 'Drobeta', 'R2': 'Rimnicu Vilcea', 'R3': 'Pitesti'};
+        self.state_space['Rimnicu Vilcea'] = {'R1': 'Sibiu', 'R2': 'Pitesti', 'R3': 'Craiova'};
+        self.state_space['Sibiu'] = {'R1': 'Arad', 'R2': 'Fagaras', 'R3': 'Oradea', 'R4': 'Rimnicu Vilcea'};
+        self.state_space['Fagaras'] = {'R1': 'Sibiu', 'R2': 'Bucharest'};
+        self.state_space['Pitesti'] = {'R1': 'Rimnicu Vilcea', 'R2': 'Craiova', 'R3': 'Bucharest'};
+        self.state_space['Bucharest'] = {'R1': 'Fagaras', 'R2': 'Pitesti', 'R3': 'Giurgiu', 'R4': 'Urziceni'};
+        self.state_space['Giurgiu'] = {'R1': 'Bucharest'};
+        self.state_space['Urziceni'] = {'R1': 'Bucharest', 'R2': 'Valsui', 'R3': 'Hirsova'};
+        self.state_space['Hirsova'] = {'R1': 'Eforie', 'R2': 'Urziceni'};
+        self.state_space['Eforie'] = {'R1': 'Hirsova'};
+        self.state_space['Valsui'] = {'R1': 'Urziceni', 'R2': 'Iasi'};
+        self.state_space['Iasi'] = {'R1': 'Valsui', 'R2': 'Neamt'};
+        self.state_space['Neamt'] = {'R1': 'Iasi'};
+        self.state_space['Mehandia'] = {'R1': 'Lugoj', 'R2': 'Drobeta'};
+
+        self.step_cost = {};
+        self.step_cost['Arad'] = {'R1': 75, 'R2': 140, 'R3': 118};
+        self.step_cost['Zerind'] = {'R1': 71, 'R2': 75};
+        self.step_cost['Oradea'] = {'R1': 152, 'R2': 71};
+        self.step_cost['Timisoara'] = {'R1': 111, 'R2': 118};
+        self.step_cost['Lugoj'] = {'R1': 111, 'R2': 70};
+        self.step_cost['Drobeta'] = {'R1': 75, 'R2': 120};
+        self.step_cost['Craiova'] = {'R1': 120, 'R2': 146, 'R3': 138};
+        self.step_cost['Rimnicu Vilcea'] = {'R1': 80, 'R3': 97, 'R4': 146};
+        self.step_cost['Sibiu'] = {'R1': 140, 'R2': 99, 'R3': 151, 'R4': 80};
+        self.step_cost['Fagaras'] = {'R1': 99, 'R2': 211};
+        self.step_cost['Pitesti'] = {'R1': 97, 'R2': 138, 'R3': 101};
+        self.step_cost['Bucharest'] = {'R1': 211, 'R2': 101, 'R3': 90, 'R4': 85};
+        self.step_cost['Giurgiu'] = {'R1': 90};
+        self.step_cost['Urziceni'] = {'R1': 85, 'R2': 142, 'R3': 98};
+        self.step_cost['Hirsova'] = {'R1': 86, 'R2': 98};
+        self.step_cost['Eforie'] = {'R1': 86};
+        self.step_cost['Valsui'] = {'R1': 142, 'R2': 92};
+        self.step_cost['Iasi'] = {'R1': 92, 'R2': 87};
+        self.step_cost['Neamt'] = {'R1': 87};
+        self.step_cost['Mehandia'] = {'R1': 70, 'R2': 75};
+
+        self.heuristic = {}
+        self.heuristic['Arad'] = 366
+        self.heuristic['Zerind'] = 374
+        self.heuristic['Oradea'] = 380
+        self.heuristic['Timisoara'] = 329
+        self.heuristic['Lugoj'] = 244
+        self.heuristic['Drobeta'] = 242
+        self.heuristic['Craiova'] = 160
+        self.heuristic['Rimnicu Vilcea'] = 193
+        self.heuristic['Sibiu'] = 253
+        self.heuristic['Fagaras'] = 176
+        self.heuristic['Pitesti'] = 100
+        self.heuristic['Bucharest'] = 0
+        self.heuristic['Giurgiu'] = 77
+        self.heuristic['Urziceni'] = 80
+        self.heuristic['Hirsova'] = 151
+        self.heuristic['Eforie'] = 161
+        self.heuristic['Valsui'] = 199
+        self.heuristic['Iasi'] = 226
+        self.heuristic['Neamt'] = 234
+        self.heuristic['Mehandia'] = 241
+
+    def Actions(self, state):
+        lst = self.state_space[state].keys()
+        return lst
+
+    def Result(self, state, action):
+        return self.state_space[state][action]
+
+    def Goal_test(self, state):
+        return state == self.goal_state
+
+    def Path_cost(self, state, action):
+        return self.step_cost[state][action]
+
+    def h(self, state):
+        return self.heuristic[state]
+
+
+# ______________________________________________________________________________
+class Node:
+
+    def __init__(self, state, parent=None, action=None, path_cost=0, heuristic=0):
+        """Create a search tree Node, derived from a parent by an action."""
+        self.state = state
+        self.parent = parent
+        self.action = action
+        self.path_cost = path_cost
+        self.h= heuristic
+        self.f= self.h+self.path_cost 
+
+    def __repr__(self):
+        return "<Node {} {} {}>".format(self.state, self.path_cost, self.f)
+
+    def __lt__(self, node):
+        return isinstance(node, Node) and self.state < node.state
+
+    def __eq__(self, other):
+        return isinstance(other, Node) and self.state == other.state
+
+
+# -----------------------------------------------------------------------------
+
+def child_node(problem, parent, action):
+    next_state = problem.Result(parent.state, action)
+    step_cost = problem.Path_cost(parent.state, action)
+    heuristic_cost = problem.h(next_state)
+    next_node = Node(next_state, parent, action, parent.path_cost + int(step_cost), heuristic_cost)
+    return next_node
+
+
+# Problem ends here
+def solution(node):
+    path_back = []
+    while node and type(node) == Node:
+        path_back.append(node)
+        node = node.parent
+    for n in reversed(path_back):
+        print(n)
+
+
+# ______________________________________________________________________________
+
+def Astaric(problem):
+    heuristic_cost = problem.h(problem.initial_state)
+    node = Node(problem.initial_state, heuristic_cost)
+    if problem.Goal_test(node.state): return solution(node)
+    frontier = Priority_Queue()
+    frontier.enqueue(node)
+    explored = []
+    while True:
+        if not frontier: return print('Failure')
+        node = frontier.dequeue()
+        explored.append(node.state)
+        for action in problem.Actions(node.state):
+            child = child_node(problem, node, action)
+            if child.state not in explored and child not in frontier.back:
+                if problem.Goal_test(child.state):  return solution(child)
+                frontier.enqueue(child)
+            # End of if
+        # End of for
+    # End of while
+
+
+# ____________________________________________________________________________
+
+
+p = Problem('Giurgiu', 'Rimnicu Vilcea')
+
+Astaric(p)
+
+
+
+
+
+
+
+
+

From c35cf484d2c51f6e0c4f0964bc3e6d89bc621148 Mon Sep 17 00:00:00 2001
From: Syed Fasih Uddin <sssfasih@gmail.com>
Date: Sat, 23 Oct 2021 17:18:20 +0500
Subject: [PATCH 072/232] Initial code

---
 Python/Billing System.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Python/Billing System.py

diff --git a/Python/Billing System.py b/Python/Billing System.py
new file mode 100644
index 0000000..e69de29

From eda8434eb75964f5d6debbe0449985f28a9e6632 Mon Sep 17 00:00:00 2001
From: Syed Fasih Uddin <sssfasih@gmail.com>
Date: Sat, 23 Oct 2021 17:40:37 +0500
Subject: [PATCH 073/232] optimized code for unlimited order items

---
 Python/Billing System.py | 43 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 43 insertions(+)

diff --git a/Python/Billing System.py b/Python/Billing System.py
index e69de29..3ce81bb 100644
--- a/Python/Billing System.py	
+++ b/Python/Billing System.py	
@@ -0,0 +1,43 @@
+burgers=[]
+quan=[]
+def PrintHead():
+    print("             ABC Burgers")
+    print("        Pakistan Road,Karachi")
+    print("=====================================")
+
+
+# Data Entry:
+rates = {"cheese": 110, "zinger": 160, "abc special": 250, "chicken": 120}
+# Front-End:
+def TakeInputs():
+    b = input("Burger Type:").lower()
+    if b not in rates.keys():
+        print("Burger Type Not Recognized")
+        print("Kindly try again, or add the new burger in Rates list")
+        TakeInputs()
+        return
+    q = int(input("Quantity:"))
+    burgers.append(b)
+    quan.append(q)
+    yn = input("Y to add another burger:")
+    if yn == "y" or yn=="Y" :
+        TakeInputs()
+    else:
+        print("Data Collection Complete!")
+
+
+def receipt():
+    total = 0
+    print("{0:13} {1} {2:9} {3:9}".format("Burger","Rate","    Quantity","  Price"))
+    for index,each_burger in enumerate(burgers):
+        print("{0:13}".format(burgers[index].title()), rates[burgers[index]], "{0:9}".format(quan[index]),
+              "{0:9}".format(rates[burgers[index]] * quan[index]))
+        total += (rates[burgers[index]] * quan[index])
+
+    print("-------------------------------------")
+    print("                        Total:", total)
+
+PrintHead()
+TakeInputs()
+PrintHead()
+receipt()
\ No newline at end of file

From a84e0025da20d40575dfb4a1b68d4bc70022b504 Mon Sep 17 00:00:00 2001
From: Syed Fasih Uddin <sssfasih@gmail.com>
Date: Sat, 23 Oct 2021 18:26:50 +0500
Subject: [PATCH 074/232] Multiplication Table

---
 Python/Maths Table Generator.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Python/Maths Table Generator.py

diff --git a/Python/Maths Table Generator.py b/Python/Maths Table Generator.py
new file mode 100644
index 0000000..e69de29

From 564e820449f13dbc7200df4eb17c68a62c28a9f7 Mon Sep 17 00:00:00 2001
From: Syed Fasih Uddin <sssfasih@gmail.com>
Date: Sat, 23 Oct 2021 18:30:25 +0500
Subject: [PATCH 075/232] Multiplication Table

---
 Python/Maths Table Generator.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/Python/Maths Table Generator.py b/Python/Maths Table Generator.py
index e69de29..ae896ca 100644
--- a/Python/Maths Table Generator.py	
+++ b/Python/Maths Table Generator.py	
@@ -0,0 +1,8 @@
+# Take input from
+number=int(input("Enter Number for Multiplication Table:"))
+
+# i is iterator that starts from 1 and ends at 10
+for i in range(1,11):
+    # f'' syntex in python is of formatted strings
+    # you can add variables in these using { } notation
+    print(f"{number} X {i} = {number*i}")

From d355459a6915830fb1e7ec09ea3b62e0f657ca4d Mon Sep 17 00:00:00 2001
From: Syed Fasih Uddin <sssfasih@gmail.com>
Date: Sun, 24 Oct 2021 01:08:33 +0500
Subject: [PATCH 076/232] Numerals to Words

---
 Python/Number To Words.py | 36 ++++++++++++++++++++++++++++++++++++
 1 file changed, 36 insertions(+)
 create mode 100644 Python/Number To Words.py

diff --git a/Python/Number To Words.py b/Python/Number To Words.py
new file mode 100644
index 0000000..0532233
--- /dev/null
+++ b/Python/Number To Words.py	
@@ -0,0 +1,36 @@
+# Works from Zero til One Thousand!
+def InWords(num):
+    # We must define all unique numbers
+    TillTwelve = {0: "zero", 1: "one", 2: "two", 3: "three", 4: "four", 5: "five", 6: "six", 7: "seven", 8: "eight",
+                  9: "nine", 10: "ten", 11: "eleven", 12: "twelve", 13: "thirteen", 15: "fifteen"}
+    MultiplesOfTen = {20: "twenty", 30: "thirty", 40: "forty", 50: "fifty", 60: "sixty", 70: "seventy", 80: "eighty",
+                      90: "ninty", 100: "one hundred"}
+
+    if type(num) != int:
+        return "Invalid Input"
+    else:
+        if num < 20:
+            try:
+                return (TillTwelve[num])
+            except KeyError:
+                return (TillTwelve[int(str(num)[1])] + "teen")
+        elif num % 10 == 0 and not (num > 100):
+            return (MultiplesOfTen[num])
+
+        elif num < 100:
+
+            return (MultiplesOfTen[int(str(num)[0] + "0")] + " " + TillTwelve[int(str(num)[1])])
+
+
+        elif num < 1000:
+            try:
+                return (TillTwelve[int(str(num)[0])] + " hundred and " + TillTwelve[int(str(num)[1:])])
+            except KeyError:
+                broken_number = InWords(int(str(num)[1:]))
+                return (TillTwelve[int(str(num)[0])] + " hundred and " + broken_number)
+
+        else:
+            return "One Thousand! (or above)"
+
+print(InWords(int(input("Enter Any number:"))))
+input()

From d12d4ad172a318342fbcf1cf058cf4198f5242b0 Mon Sep 17 00:00:00 2001
From: Syed Fasih Uddin <sssfasih@gmail.com>
Date: Sun, 24 Oct 2021 01:20:01 +0500
Subject: [PATCH 077/232] Bill Splitter

---
 Python/Bill Splitter.py | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 Python/Bill Splitter.py

diff --git a/Python/Bill Splitter.py b/Python/Bill Splitter.py
new file mode 100644
index 0000000..4c34657
--- /dev/null
+++ b/Python/Bill Splitter.py	
@@ -0,0 +1,8 @@
+# Bill Splitter for Restaurants
+
+total = int(input("Enter Total Bill Amount:"))
+friends = int(input("Enter number of Friends:"))
+
+eachPays = total/friends
+
+print("Each person pays",round(eachPays, 2),'/- Rupees')
\ No newline at end of file

From 65c64c265610611507beb5851a6092b0aea9a936 Mon Sep 17 00:00:00 2001
From: Ammar Ayoubi <59621670+ammarayoubi@users.noreply.github.com>
Date: Sun, 24 Oct 2021 13:24:11 +0500
Subject: [PATCH 078/232] Create Dart program to check if a string is
 palindrome

---
 ...rt program to check if a string is palindrome | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)
 create mode 100644 Dart/Dart program to check if a string is palindrome

diff --git a/Dart/Dart program to check if a string is palindrome b/Dart/Dart program to check if a string is palindrome
new file mode 100644
index 0000000..8fc409e
--- /dev/null
+++ b/Dart/Dart program to check if a string is palindrome	
@@ -0,0 +1,16 @@
+import 'dart:io';
+
+void main() {
+  print('Enter Any String ');
+  String name = stdin.readLineSync()!;
+
+  var sb = StringBuffer();
+
+  for (int i = name.length - 1; i >= 0; i--) {
+    sb.write(name[i]);
+  }
+
+  if (name == sb.toString()) {
+    print('right');
+  }
+}

From 281a5532f17e7dc94d698bab5ea4424a643404b1 Mon Sep 17 00:00:00 2001
From: Ammar Ayoubi <59621670+ammarayoubi@users.noreply.github.com>
Date: Sun, 24 Oct 2021 13:45:12 +0500
Subject: [PATCH 079/232] Create Dart program to check if a given number is
 prime or not.

---
 ...o check if a given number is prime or not. | 29 +++++++++++++++++++
 1 file changed, 29 insertions(+)
 create mode 100644 Dart/Dart program to check if a given number is prime or not.

diff --git a/Dart/Dart program to check if a given number is prime or not. b/Dart/Dart program to check if a given number is prime or not.
new file mode 100644
index 0000000..c6135aa
--- /dev/null
+++ b/Dart/Dart program to check if a given number is prime or not.	
@@ -0,0 +1,29 @@
+import 'dart:io';
+
+void main() {
+  stdout.write("Please Enter a number: ");
+  String? data = stdin.readLineSync();
+  if (data != null && isNumeric(data)) {
+    print(data);
+    int chosenNumber = int.parse(data);
+
+    checkPrime(chosenNumber);
+  }
+}
+
+bool isNumeric(String str) {
+  return double.tryParse(str) != null;
+}
+
+void checkPrime(int number) {
+  // List comprehensions
+  List<int> a = [
+    for (var i = 1; i <= number; i++)
+      if (number % i == 0) i
+  ];
+
+  // Check for prime
+  a.length == 2
+      ? print("Given number is prime number")
+      : print("Given number is not a prime");
+}

From d77a9caa521cad092fec93c1f458f183e1df257a Mon Sep 17 00:00:00 2001
From: Ammar Ayoubi <59621670+ammarayoubi@users.noreply.github.com>
Date: Sun, 24 Oct 2021 13:53:34 +0500
Subject: [PATCH 080/232] Create Dart program to check if a number is prime or
 not.

---
 ...gram to check if a number is prime or not. | 29 +++++++++++++++++++
 1 file changed, 29 insertions(+)
 create mode 100644 Dart/Dart program to check if a number is prime or not.

diff --git a/Dart/Dart program to check if a number is prime or not. b/Dart/Dart program to check if a number is prime or not.
new file mode 100644
index 0000000..c6135aa
--- /dev/null
+++ b/Dart/Dart program to check if a number is prime or not.	
@@ -0,0 +1,29 @@
+import 'dart:io';
+
+void main() {
+  stdout.write("Please Enter a number: ");
+  String? data = stdin.readLineSync();
+  if (data != null && isNumeric(data)) {
+    print(data);
+    int chosenNumber = int.parse(data);
+
+    checkPrime(chosenNumber);
+  }
+}
+
+bool isNumeric(String str) {
+  return double.tryParse(str) != null;
+}
+
+void checkPrime(int number) {
+  // List comprehensions
+  List<int> a = [
+    for (var i = 1; i <= number; i++)
+      if (number % i == 0) i
+  ];
+
+  // Check for prime
+  a.length == 2
+      ? print("Given number is prime number")
+      : print("Given number is not a prime");
+}

From 53989b2eb71bd4653b250c9c71cdcca0c79f4ba7 Mon Sep 17 00:00:00 2001
From: Ammar Ayoubi <59621670+ammarayoubi@users.noreply.github.com>
Date: Sun, 24 Oct 2021 14:05:25 +0500
Subject: [PATCH 081/232] Delete Dart program to check if a given number is
 prime or not.

---
 ...o check if a given number is prime or not. | 29 -------------------
 1 file changed, 29 deletions(-)
 delete mode 100644 Dart/Dart program to check if a given number is prime or not.

diff --git a/Dart/Dart program to check if a given number is prime or not. b/Dart/Dart program to check if a given number is prime or not.
deleted file mode 100644
index c6135aa..0000000
--- a/Dart/Dart program to check if a given number is prime or not.	
+++ /dev/null
@@ -1,29 +0,0 @@
-import 'dart:io';
-
-void main() {
-  stdout.write("Please Enter a number: ");
-  String? data = stdin.readLineSync();
-  if (data != null && isNumeric(data)) {
-    print(data);
-    int chosenNumber = int.parse(data);
-
-    checkPrime(chosenNumber);
-  }
-}
-
-bool isNumeric(String str) {
-  return double.tryParse(str) != null;
-}
-
-void checkPrime(int number) {
-  // List comprehensions
-  List<int> a = [
-    for (var i = 1; i <= number; i++)
-      if (number % i == 0) i
-  ];
-
-  // Check for prime
-  a.length == 2
-      ? print("Given number is prime number")
-      : print("Given number is not a prime");
-}

From fa6f798d192f4340a36c3134eb48495337321a4c Mon Sep 17 00:00:00 2001
From: Ammar Ayoubi <59621670+ammarayoubi@users.noreply.github.com>
Date: Sun, 24 Oct 2021 14:05:45 +0500
Subject: [PATCH 082/232] Delete Dart program to check if a number is prime or
 not.

---
 ...gram to check if a number is prime or not. | 29 -------------------
 1 file changed, 29 deletions(-)
 delete mode 100644 Dart/Dart program to check if a number is prime or not.

diff --git a/Dart/Dart program to check if a number is prime or not. b/Dart/Dart program to check if a number is prime or not.
deleted file mode 100644
index c6135aa..0000000
--- a/Dart/Dart program to check if a number is prime or not.	
+++ /dev/null
@@ -1,29 +0,0 @@
-import 'dart:io';
-
-void main() {
-  stdout.write("Please Enter a number: ");
-  String? data = stdin.readLineSync();
-  if (data != null && isNumeric(data)) {
-    print(data);
-    int chosenNumber = int.parse(data);
-
-    checkPrime(chosenNumber);
-  }
-}
-
-bool isNumeric(String str) {
-  return double.tryParse(str) != null;
-}
-
-void checkPrime(int number) {
-  // List comprehensions
-  List<int> a = [
-    for (var i = 1; i <= number; i++)
-      if (number % i == 0) i
-  ];
-
-  // Check for prime
-  a.length == 2
-      ? print("Given number is prime number")
-      : print("Given number is not a prime");
-}

From 3fad1a301a39fffb7b5322fac0cdd5951ea15d5f Mon Sep 17 00:00:00 2001
From: Ammar Ayoubi <59621670+ammarayoubi@users.noreply.github.com>
Date: Sun, 24 Oct 2021 14:17:44 +0500
Subject: [PATCH 083/232] Create Dart program to check if a number is prime or
 not.

---
 ...gram to check if a number is prime or not. | 29 +++++++++++++++++++
 1 file changed, 29 insertions(+)
 create mode 100644 Dart/Dart program to check if a number is prime or not.

diff --git a/Dart/Dart program to check if a number is prime or not. b/Dart/Dart program to check if a number is prime or not.
new file mode 100644
index 0000000..c6135aa
--- /dev/null
+++ b/Dart/Dart program to check if a number is prime or not.	
@@ -0,0 +1,29 @@
+import 'dart:io';
+
+void main() {
+  stdout.write("Please Enter a number: ");
+  String? data = stdin.readLineSync();
+  if (data != null && isNumeric(data)) {
+    print(data);
+    int chosenNumber = int.parse(data);
+
+    checkPrime(chosenNumber);
+  }
+}
+
+bool isNumeric(String str) {
+  return double.tryParse(str) != null;
+}
+
+void checkPrime(int number) {
+  // List comprehensions
+  List<int> a = [
+    for (var i = 1; i <= number; i++)
+      if (number % i == 0) i
+  ];
+
+  // Check for prime
+  a.length == 2
+      ? print("Given number is prime number")
+      : print("Given number is not a prime");
+}

From c755efce85ec15c333af874707c80f1f1a1673a6 Mon Sep 17 00:00:00 2001
From: Ammar Ayoubi <59621670+ammarayoubi@users.noreply.github.com>
Date: Sun, 24 Oct 2021 14:44:04 +0500
Subject: [PATCH 084/232] Create Dart program to upload file to firebase
 storage.

---
 ...rogram to upload file to firebase storage. | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)
 create mode 100644 Dart/Dart program to upload file to firebase storage.

diff --git a/Dart/Dart program to upload file to firebase storage. b/Dart/Dart program to upload file to firebase storage.
new file mode 100644
index 0000000..189b8bc
--- /dev/null
+++ b/Dart/Dart program to upload file to firebase storage.	
@@ -0,0 +1,19 @@
+import 'dart:io';
+
+import 'package:firebase_storage/firebase_storage.dart';
+
+class UploadFileToStorage{
+
+  late Reference _storageReference;
+
+  Future uploadFileToStorage(File imageFile, String destination) async {
+
+    _storageReference = FirebaseStorage.instance
+        .ref(destination)
+        .child('${DateTime.now().millisecondsSinceEpoch}');
+    UploadTask storageUploadTask = _storageReference.putFile(imageFile);
+    var url = await (await storageUploadTask).ref.getDownloadURL();
+
+    return url;
+  }
+}

From 33ef8f9fb7e5b692522b6fb403025e4dfe9bd549 Mon Sep 17 00:00:00 2001
From: Ammar Ayoubi <59621670+ammarayoubi@users.noreply.github.com>
Date: Sun, 24 Oct 2021 15:01:37 +0500
Subject: [PATCH 085/232] Create Dart program to compress image

---
 Dart/Dart program to compress image | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)
 create mode 100644 Dart/Dart program to compress image

diff --git a/Dart/Dart program to compress image b/Dart/Dart program to compress image
new file mode 100644
index 0000000..a49e8a6
--- /dev/null
+++ b/Dart/Dart program to compress image	
@@ -0,0 +1,20 @@
+import 'dart:io';
+
+void main(){
+void compressImage() async {
+    print('starting compression');
+    final tempDir = await getTemporaryDirectory();
+    final path = tempDir.path;
+    int rand = Random().nextInt(10000);
+  
+    Im.Image? image = Im.decodeImage(file!.readAsBytesSync());
+    Im.copyResize(image!,height: 1080,width: 1080);
+  
+    var newim2 = new File('$path/img_$rand.jpg')
+      ..writeAsBytesSync(Im.encodeJpg(image, quality: 25));
+  
+  
+      file = newim2;
+  
+    print('done');
+  }

From 9afa4ce8c29320a80f449398df2b181015406cbb Mon Sep 17 00:00:00 2001
From: MalikMuneebshahid86
 <74771568+MalikMuneebshahid86@users.noreply.github.com>
Date: Sun, 24 Oct 2021 19:33:46 +0500
Subject: [PATCH 086/232] Create bricksort.py

---
 Python/bricksort.py | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)
 create mode 100644 Python/bricksort.py

diff --git a/Python/bricksort.py b/Python/bricksort.py
new file mode 100644
index 0000000..a186d8b
--- /dev/null
+++ b/Python/bricksort.py
@@ -0,0 +1,25 @@
+def oddEvenSort(arr, n): 
+    # Initially array is unsorted 
+    isSorted = 0
+    while isSorted == 0: 
+        isSorted = 1
+        temp = 0
+        for i in range(1, n-1, 2): 
+            if arr[i] > arr[i+1]: 
+                arr[i], arr[i+1] = arr[i+1], arr[i] 
+                isSorted = 0
+                  
+        for i in range(0, n-1, 2): 
+            if arr[i] > arr[i+1]: 
+                arr[i], arr[i+1] = arr[i+1], arr[i] 
+                isSorted = 0
+      
+    return
+  
+  
+arr = [34, 2, 10, -9] 
+n = len(arr) 
+  
+oddEvenSort(arr, n); 
+for i in range(0, n): 
+    print(arr[i], end =" ") 

From 9313dfa03194fe6d5b90111112cf6f0d55578898 Mon Sep 17 00:00:00 2001
From: Syed Fasih Uddin <sssfasih@gmail.com>
Date: Sun, 24 Oct 2021 20:29:02 +0500
Subject: [PATCH 087/232] Added Expected output

---
 Python/Maths Table Generator.py | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)

diff --git a/Python/Maths Table Generator.py b/Python/Maths Table Generator.py
index ae896ca..5f0b33b 100644
--- a/Python/Maths Table Generator.py	
+++ b/Python/Maths Table Generator.py	
@@ -6,3 +6,19 @@
     # f'' syntex in python is of formatted strings
     # you can add variables in these using { } notation
     print(f"{number} X {i} = {number*i}")
+
+'''
+Expected Output:
+Enter Number for Multiplication Table: 3
+3 X 1 = 3
+3 X 2 = 6
+3 X 3 = 9
+3 X 4 = 12
+3 X 5 = 15
+3 X 6 = 18
+3 X 7 = 21
+3 X 8 = 24
+3 X 9 = 27
+3 X 10 = 30
+
+'''
\ No newline at end of file

From 3e2aa2d6f33488d21a8138e7408e17a18df74469 Mon Sep 17 00:00:00 2001
From: Syed Fasih Uddin <sssfasih@gmail.com>
Date: Sun, 24 Oct 2021 20:59:57 +0500
Subject: [PATCH 088/232] Added Expected outputs and extensive commenting

---
 Python/Number To Words.py | 46 +++++++++++++++++++++++++++++----------
 1 file changed, 35 insertions(+), 11 deletions(-)

diff --git a/Python/Number To Words.py b/Python/Number To Words.py
index 0532233..acbf23f 100644
--- a/Python/Number To Words.py	
+++ b/Python/Number To Words.py	
@@ -1,8 +1,10 @@
 # Works from Zero til One Thousand!
 def InWords(num):
     # We must define all unique numbers
-    TillTwelve = {0: "zero", 1: "one", 2: "two", 3: "three", 4: "four", 5: "five", 6: "six", 7: "seven", 8: "eight",
+    TillFifteen = {0: "zero", 1: "one", 2: "two", 3: "three", 4: "four", 5: "five", 6: "six", 7: "seven", 8: "eight",
                   9: "nine", 10: "ten", 11: "eleven", 12: "twelve", 13: "thirteen", 15: "fifteen"}
+    # Multiples of ten to add before unique numbers
+    # For Example TWENTY four, THIRTY four, SEVENTY four
     MultiplesOfTen = {20: "twenty", 30: "thirty", 40: "forty", 50: "fifty", 60: "sixty", 70: "seventy", 80: "eighty",
                       90: "ninty", 100: "one hundred"}
 
@@ -11,26 +13,48 @@ def InWords(num):
     else:
         if num < 20:
             try:
-                return (TillTwelve[num])
+                # Try Unique List
+                return (TillFifteen[num])
             except KeyError:
-                return (TillTwelve[int(str(num)[1])] + "teen")
+                # sixTeen till nineTeen are returned by using
+                # last digit added with added keyword teen
+                # example: sixteen , seventeen, eighteen
+                return (TillFifteen[int(str(num)[1])] + "teen")
         elif num % 10 == 0 and not (num > 100):
+            # If number is exact multiple of ten
+            # Fetch it from the MultiplesOfTen dictionary
             return (MultiplesOfTen[num])
 
         elif num < 100:
-
-            return (MultiplesOfTen[int(str(num)[0] + "0")] + " " + TillTwelve[int(str(num)[1])])
+            # From 21 and above, First number represent multiple of ten
+            # rest of number is handled in above code (Recursive Call)
+            multiple_of_ten = MultiplesOfTen[int(str(num)[0] + "0")]
+            return (multiple_of_ten + " " + InWords(int(str(num)[1:])))
 
 
         elif num < 1000:
-            try:
-                return (TillTwelve[int(str(num)[0])] + " hundred and " + TillTwelve[int(str(num)[1:])])
-            except KeyError:
-                broken_number = InWords(int(str(num)[1:]))
-                return (TillTwelve[int(str(num)[0])] + " hundred and " + broken_number)
+            # From one hundred and above First number represents
+            # unique number with added statement ' hundred and '
+            # For Example : Three hundred and four, Six hundred and four
+            # rest of number is handled in above code (Recursive Call)
+            unique_number = TillFifteen[int(str(num)[0])]
+            return (unique_number + " hundred and " + InWords(int(str(num)[1:])))
 
         else:
+            # TODO
             return "One Thousand! (or above)"
 
 print(InWords(int(input("Enter Any number:"))))
-input()
+
+'''
+Expected Outputs:
+
+Enter Any number:5
+five
+
+Enter Any number:78
+seventy eight
+
+Enter Any number:235
+two hundred and thirty five
+'''

From 03b783f614ba16209b765b94996f0a75a9490ac4 Mon Sep 17 00:00:00 2001
From: rzmk <mueez25@gmail.com>
Date: Sun, 24 Oct 2021 12:02:36 -0400
Subject: [PATCH 089/232] Add transposeMatrix.js

---
 Javascript/transposeMatrix.js | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)
 create mode 100644 Javascript/transposeMatrix.js

diff --git a/Javascript/transposeMatrix.js b/Javascript/transposeMatrix.js
new file mode 100644
index 0000000..65d0c79
--- /dev/null
+++ b/Javascript/transposeMatrix.js
@@ -0,0 +1,27 @@
+// Transpose a matrix (2D array) of numbers (integers or floats) and return the result.
+// The matrix is transposed by swapping the rows and columns. The result is a new matrix.
+// The original matrix is not changed.
+// The input matrix is a 2D array of numbers. The result is a 2D array of numbers.
+const transposeMatrix = (matrix) => {
+    let result = [];
+    for (let i = 0; i < matrix[0].length; i++) { // loop through columns
+        result.push([]); // add a new row to the result matrix
+    }
+    for (let i = 0; i < matrix.length; i++) { // loop through rows of the input matrix
+        for (let j = 0; j < matrix[0].length; j++) { // loop through columns of the input matrix
+            result[j][i] = matrix[i][j]; // swap rows and columns in the result matrix and add the value to the result matrix
+        }
+    }
+    return result;
+}
+
+// Driver to test the transposeMatrix function.
+console.log(transposeMatrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]])); // Output: [[1, 4, 7], [2, 5, 8], [3, 6, 9]]
+// From
+//  [[ 1, 2, 3 ],
+//   [ 4, 5, 6 ],
+//   [ 7, 8, 9 ]]
+// To
+// → [[1, 4, 7],
+//    [2, 5, 8],
+//    [3, 6, 9]]
\ No newline at end of file

From 2316405ee5578a66092cbc27c30bc53b49138ed0 Mon Sep 17 00:00:00 2001
From: Syed Fasih Uddin <sssfasih@gmail.com>
Date: Sun, 24 Oct 2021 21:09:10 +0500
Subject: [PATCH 090/232] Added Expected output and comments

---
 Python/Bill Splitter.py | 21 ++++++++++++++++++++-
 1 file changed, 20 insertions(+), 1 deletion(-)

diff --git a/Python/Bill Splitter.py b/Python/Bill Splitter.py
index 4c34657..6744602 100644
--- a/Python/Bill Splitter.py	
+++ b/Python/Bill Splitter.py	
@@ -1,8 +1,27 @@
 # Bill Splitter for Restaurants
+# Task is to divide the total bill amount in friends equally
 
+# We take two inputs: bill amount, and number of friends
 total = int(input("Enter Total Bill Amount:"))
 friends = int(input("Enter number of Friends:"))
 
+# Then we calculate how much each person pays
 eachPays = total/friends
 
-print("Each person pays",round(eachPays, 2),'/- Rupees')
\ No newline at end of file
+# we show it on screen
+# round function rounds it the answer to given decimal places
+# round ( 123.45679,2 ) will round number to 2 decimal places
+print("Each person pays",round(eachPays, 2),'/- Rupees')
+
+'''
+Expected Output:
+
+Enter Total Bill Amount:2000
+Enter number of Friends:4
+Each person pays 500.0 /- Rupees
+
+Enter Total Bill Amount:8000
+Enter number of Friends:2
+Each person pays 4000.0 /- Rupees
+
+'''

From c473bef5304e7bfbb23797236fe441bb1dc810c2 Mon Sep 17 00:00:00 2001
From: rzmk <mueez25@gmail.com>
Date: Sun, 24 Oct 2021 12:33:30 -0400
Subject: [PATCH 091/232] Add GitHub PFP scraper

---
 Python/GitHub_PFP_URL.py | 15 +++++++++++++++
 1 file changed, 15 insertions(+)
 create mode 100644 Python/GitHub_PFP_URL.py

diff --git a/Python/GitHub_PFP_URL.py b/Python/GitHub_PFP_URL.py
new file mode 100644
index 0000000..400fd36
--- /dev/null
+++ b/Python/GitHub_PFP_URL.py
@@ -0,0 +1,15 @@
+# Get GitHub Profile Picture URL from username
+
+# Import required modules for program to run correctly and display output to user in terminal
+import requests # Import requests module to send GET request to GitHub API
+import json # Import json module to load JSON data from GitHub API
+
+# Define function to get GitHub Profile Picture URL from username and return it to user in terminal
+def getProfilePicture(username): # Define function with parameter username
+    url = "https://api.github.com/users/" + username # Define url variable with GitHub API URL and username parameter
+    r = requests.get(url) # Send GET request to URL and assign to r variable (r = response)
+    data = json.loads(r.text) # Load JSON data from r.text and assign to data variable (data = response data)
+    return data["avatar_url"] # Return the avatar_url from data
+
+# Driver to test function
+print(getProfilePicture('MLSA-Mehran-UET')) # Print the URL of the profile picture
\ No newline at end of file

From 2429b790e369fd3371f2811cdab0f68cbd2101e8 Mon Sep 17 00:00:00 2001
From: Ali Haider <alihaider70002@gmail.com>
Date: Sun, 24 Oct 2021 21:51:36 -0700
Subject: [PATCH 092/232] Create palindrome.py

---
 Python/palindrome.py | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 Python/palindrome.py

diff --git a/Python/palindrome.py b/Python/palindrome.py
new file mode 100644
index 0000000..e5e5f60
--- /dev/null
+++ b/Python/palindrome.py
@@ -0,0 +1,10 @@
+def checkIfPalindrome(s):
+    return s == s[::-1]
+    
+s = "A Santa Lived As a Devil At NASA"
+ans = checkIfPalindrome(s)
+ 
+if ans:
+    print("Yes")
+else:
+    print("No")

From 8facfdda472af6a9c15c43ef46b1e38d23a908e8 Mon Sep 17 00:00:00 2001
From: rzmk <mueez25@gmail.com>
Date: Mon, 25 Oct 2021 07:35:34 -0400
Subject: [PATCH 093/232] Longest common prefix in a list of words

---
 Python/longestCommonPrefix.py | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)
 create mode 100644 Python/longestCommonPrefix.py

diff --git a/Python/longestCommonPrefix.py b/Python/longestCommonPrefix.py
new file mode 100644
index 0000000..1263fe1
--- /dev/null
+++ b/Python/longestCommonPrefix.py
@@ -0,0 +1,19 @@
+# Longest Common Prefix
+def longestCommonPrefix(words): # Input: List of Strings
+    if not words: # if words is empty
+        return "" # return empty string
+    if len(words) == 1: # if words has only one element
+        return words[0] # return the element
+    prefix = words[0] # set prefix to the first element
+    for i in range(1, len(words)): # for each element in words
+        while words[i].find(prefix) != 0: # while the element does not start with prefix
+            prefix = prefix[:-1] # remove the last character of prefix
+            if prefix == "": # if prefix is empty
+                return "" # return empty string
+    return prefix # Output: String of the longest common prefix
+
+# Driver to test the function
+print(longestCommonPrefix(["flower","flow","flight"])) # Output: "fl"
+print(longestCommonPrefix(["dog","racecar","car"])) # Output: ""
+print(longestCommonPrefix(["a"])) # Output: "a"
+print(longestCommonPrefix(["aa","a"])) # Output: "a"

From 914d564e51de2c25aac5ac4f2affc98494da0574 Mon Sep 17 00:00:00 2001
From: Junaid Ahmed <74968802+JunaidAhmed032@users.noreply.github.com>
Date: Mon, 25 Oct 2021 17:39:43 +0500
Subject: [PATCH 094/232] Create Decimal to binary Convertor.java

---
 Java/Decimal to binary Convertor.java | 32 +++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)
 create mode 100644 Java/Decimal to binary Convertor.java

diff --git a/Java/Decimal to binary Convertor.java b/Java/Decimal to binary Convertor.java
new file mode 100644
index 0000000..fed5aed
--- /dev/null
+++ b/Java/Decimal to binary Convertor.java	
@@ -0,0 +1,32 @@
+/*
+ * To change this license header, choose License Headers in Project Properties.
+ * To change this template file, choose Tools | Templates
+ * and open the template in the editor.
+ */
+package binaryconvet;
+
+import java.util.Scanner;
+
+/**
+ *
+ * @author lenovo
+ */
+public class BinaryConvet {
+public static int BinaryConverter(int Input){
+            if (Input == 0)  
+            return 0;  
+              
+        else
+          
+        return (Input % 2 + 10 *  
+               BinaryConverter(Input / 2));
+    }
+       public static void main(String[] args) {
+           Scanner s=new Scanner(System.in);
+           System.out.println("Enter a decimal number ");
+            int DecimalNumber=s.nextInt();
+            System.out.println("Binary number is :"+BinaryConverter(DecimalNumber));
+       
+    }
+    
+}

From fe9eb64a98c0809bad2cde7a4b75ecf70f37aed1 Mon Sep 17 00:00:00 2001
From: Mahnoor Naveed <mahnoornaveed74@gmail.com>
Date: Tue, 26 Oct 2021 08:03:06 +0500
Subject: [PATCH 095/232] add javascript template literals

---
 Javascript/template-literals.js | 12 ++++++++++++
 1 file changed, 12 insertions(+)
 create mode 100644 Javascript/template-literals.js

diff --git a/Javascript/template-literals.js b/Javascript/template-literals.js
new file mode 100644
index 0000000..c8452a5
--- /dev/null
+++ b/Javascript/template-literals.js
@@ -0,0 +1,12 @@
+// JavaScript Template Literals 
+
+// Template Literals are enclosed with backticks ``
+// Allow variables and expression, use single and dual quotes, multiline in strings
+const years = 5
+console.log(`She has ${years} of experience in "Javascript" 
+She has worked for Multiple companies`)
+
+// OUTPUT
+
+// She has 5 of experience in "Javascript" 
+// She has worked for Multiple companies
\ No newline at end of file

From 0d08abeefcf0abec12163ca6a8fba8269f849527 Mon Sep 17 00:00:00 2001
From: Mursal Furqan <36442744+mursalfk@users.noreply.github.com>
Date: Tue, 26 Oct 2021 08:16:09 +0500
Subject: [PATCH 096/232] Update README.md

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index bfcee9c..9820c14 100644
--- a/README.md
+++ b/README.md
@@ -6,6 +6,8 @@ It is an intiiative undertaken by Student Ambassadors Club, Jamshoro for student
 1. You can contribute in terms of Code
 2. You can contribute in terms of improving Documentation
 ## Following the procedure you can make your contribution with ease.
+### Step 0
+Star and Fork this repository
 ### Step 1
 Look in the issues if the change you are doing is available and issues section is it assigned to someone or not if you can't find any issue open one. Anyone from our team will assign you the issue then you can start contributing
 ### step 2 Fork this repository

From b420080e453e8abb37e5f1f9943f4ba8e2c47fba Mon Sep 17 00:00:00 2001
From: Mahnoor Naveed <mahnoornaveed74@gmail.com>
Date: Tue, 26 Oct 2021 08:20:34 +0500
Subject: [PATCH 097/232] add recursive function in javascript

---
 recursion.js | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)
 create mode 100644 recursion.js

diff --git a/recursion.js b/recursion.js
new file mode 100644
index 0000000..9bf02c4
--- /dev/null
+++ b/recursion.js
@@ -0,0 +1,16 @@
+// Recursion in javascript
+
+// A function that call itself is called recursive function and the process called Recursion
+// There must be condition to run the recursive function otherwise the function will call indefinitely
+
+
+// Example of Recursive Function
+
+function findFactorial(num){
+  if(num === 1){
+      return num;
+  }
+  return num * findFactorial(num-1) 
+}
+
+console.log(findFactorial(6));
\ No newline at end of file

From c190488c7768cc313dad30cd3e19a4dd67456d4e Mon Sep 17 00:00:00 2001
From: Mahnoor Naveed <mahnoornaveed74@gmail.com>
Date: Tue, 26 Oct 2021 08:40:32 +0500
Subject: [PATCH 098/232] add output for the recursive function.

---
 recursion.js | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/recursion.js b/recursion.js
index 9bf02c4..6d3460a 100644
--- a/recursion.js
+++ b/recursion.js
@@ -13,4 +13,7 @@ function findFactorial(num){
   return num * findFactorial(num-1) 
 }
 
-console.log(findFactorial(6));
\ No newline at end of file
+console.log(findFactorial(6));
+
+//OUTPUT
+//720

From 724ca118bdec76cf899dee0b3e0e45f481cd7dab Mon Sep 17 00:00:00 2001
From: Junaid Ahmed <74968802+JunaidAhmed032@users.noreply.github.com>
Date: Tue, 26 Oct 2021 09:46:05 +0500
Subject: [PATCH 099/232] Update Decimal to binary Convertor.java

---
 Java/Decimal to binary Convertor.java | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/Java/Decimal to binary Convertor.java b/Java/Decimal to binary Convertor.java
index fed5aed..410d261 100644
--- a/Java/Decimal to binary Convertor.java	
+++ b/Java/Decimal to binary Convertor.java	
@@ -12,19 +12,23 @@
  * @author lenovo
  */
 public class BinaryConvet {
+// BINARY CONVERTER FUNCTION BELOW:            
 public static int BinaryConverter(int Input){
+           //CHECHING IF NUMBER IS ZERO
             if (Input == 0)  
             return 0;  
               
         else
-          
-        return (Input % 2 + 10 *  
-               BinaryConverter(Input / 2));
+          //FROMULA OF CONERTING TO BINARY
+        return (Input % 2 + 10 *  BinaryConverter(Input / 2));
     }
        public static void main(String[] args) {
+          //CREATING SCANNER CLASS OBJECT TO TAKE INPUT
            Scanner s=new Scanner(System.in);
            System.out.println("Enter a decimal number ");
+            //DECIMAL VARIABLE FRO DECIMAL NUMBER
             int DecimalNumber=s.nextInt();
+            //PRINTING AND PASSING ARGUMENTS TO BINARYCONVERTER FUNCTION
             System.out.println("Binary number is :"+BinaryConverter(DecimalNumber));
        
     }

From c726d41ef65e0daaf1b56f9c26d5f04095aa2021 Mon Sep 17 00:00:00 2001
From: Kainat-Mahar <72600530+Kainat-Mahar@users.noreply.github.com>
Date: Tue, 26 Oct 2021 21:24:34 +0500
Subject: [PATCH 100/232] Add files via upload

---
 .../Bitcoin__Price_Prediction.ipynb           | 1027 +++++++++++++++++
 1 file changed, 1027 insertions(+)
 create mode 100644 Machine Learning/Bitcoin__Price_Prediction.ipynb

diff --git a/Machine Learning/Bitcoin__Price_Prediction.ipynb b/Machine Learning/Bitcoin__Price_Prediction.ipynb
new file mode 100644
index 0000000..10cc954
--- /dev/null
+++ b/Machine Learning/Bitcoin__Price_Prediction.ipynb	
@@ -0,0 +1,1027 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Bitcoin _Price_Prediction.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FmMO__YhieOb"
+      },
+      "source": [
+        "import json\n",
+        "import requests\n",
+        "import tensorflow as tf\n",
+        "from tensorflow import  keras\n",
+        "from tensorflow.keras import layers\n",
+        "#from tensorflow.contrib import lite\n",
+        "from keras.models import Sequential\n",
+        "from keras.layers import Activation, Dense, Dropout, LSTM\n",
+        "from keras.callbacks import EarlyStopping\n",
+        "import matplotlib.pyplot as plt\n",
+        "import plotly.offline as py\n",
+        "import plotly.graph_objs as go\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "from sklearn.metrics import mean_absolute_error\n",
+        "%matplotlib inline"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FBVKRHGfigX6"
+      },
+      "source": [
+        "endpoint = 'https://min-api.cryptocompare.com/data/histoday'\n",
+        "res = requests.get(endpoint + '?fsym=BTC&tsym=USD&limit=500')\n",
+        "data = pd.DataFrame(json.loads(res.content)['Data'])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IBY6ytPNik0L",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 227
+        },
+        "outputId": "9a29c462-99a5-4081-8849-f19db1a131f5"
+      },
+      "source": [
+        "data = data.set_index('time')\n",
+        "data.index = pd.to_datetime(data.index, unit='s')\n",
+        "data.head(5)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>high</th>\n",
+              "      <th>low</th>\n",
+              "      <th>open</th>\n",
+              "      <th>volumefrom</th>\n",
+              "      <th>volumeto</th>\n",
+              "      <th>close</th>\n",
+              "      <th>conversionType</th>\n",
+              "      <th>conversionSymbol</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>time</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2019-12-21</th>\n",
+              "      <td>7213.86</td>\n",
+              "      <td>7132.82</td>\n",
+              "      <td>7203.41</td>\n",
+              "      <td>12101.12</td>\n",
+              "      <td>8.686484e+07</td>\n",
+              "      <td>7159.47</td>\n",
+              "      <td>direct</td>\n",
+              "      <td></td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2019-12-22</th>\n",
+              "      <td>7527.40</td>\n",
+              "      <td>7143.17</td>\n",
+              "      <td>7159.47</td>\n",
+              "      <td>27325.26</td>\n",
+              "      <td>2.005866e+08</td>\n",
+              "      <td>7517.58</td>\n",
+              "      <td>direct</td>\n",
+              "      <td></td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2019-12-23</th>\n",
+              "      <td>7694.65</td>\n",
+              "      <td>7275.28</td>\n",
+              "      <td>7517.58</td>\n",
+              "      <td>44770.93</td>\n",
+              "      <td>3.364250e+08</td>\n",
+              "      <td>7326.60</td>\n",
+              "      <td>direct</td>\n",
+              "      <td></td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2019-12-24</th>\n",
+              "      <td>7429.42</td>\n",
+              "      <td>7175.50</td>\n",
+              "      <td>7326.60</td>\n",
+              "      <td>27770.75</td>\n",
+              "      <td>2.030134e+08</td>\n",
+              "      <td>7260.91</td>\n",
+              "      <td>direct</td>\n",
+              "      <td></td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2019-12-25</th>\n",
+              "      <td>7280.52</td>\n",
+              "      <td>7132.30</td>\n",
+              "      <td>7260.91</td>\n",
+              "      <td>15012.21</td>\n",
+              "      <td>1.085133e+08</td>\n",
+              "      <td>7202.72</td>\n",
+              "      <td>direct</td>\n",
+              "      <td></td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "               high      low     open  ...    close  conversionType  conversionSymbol\n",
+              "time                                   ...                                           \n",
+              "2019-12-21  7213.86  7132.82  7203.41  ...  7159.47          direct                  \n",
+              "2019-12-22  7527.40  7143.17  7159.47  ...  7517.58          direct                  \n",
+              "2019-12-23  7694.65  7275.28  7517.58  ...  7326.60          direct                  \n",
+              "2019-12-24  7429.42  7175.50  7326.60  ...  7260.91          direct                  \n",
+              "2019-12-25  7280.52  7132.30  7260.91  ...  7202.72          direct                  \n",
+              "\n",
+              "[5 rows x 8 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AnPPwQsXiliC",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "21546cfe-08d4-49b0-e5bd-f06af964fb6e"
+      },
+      "source": [
+        "target_col = data['close']\n",
+        "len(target_col)\n",
+        "target_col"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "time\n",
+              "2019-12-21     7159.47\n",
+              "2019-12-22     7517.58\n",
+              "2019-12-23     7326.60\n",
+              "2019-12-24     7260.91\n",
+              "2019-12-25     7202.72\n",
+              "                ...   \n",
+              "2021-04-30    57757.97\n",
+              "2021-05-01    57843.16\n",
+              "2021-05-02    56623.51\n",
+              "2021-05-03    57195.07\n",
+              "2021-05-04    56108.65\n",
+              "Name: close, Length: 501, dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LakZtj3riThB",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "78ff2ab8-b313-45fe-d53c-7e99658abe1f"
+      },
+      "source": [
+        "target_col.describe()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count      501.000000\n",
+              "mean     20289.575749\n",
+              "std      17327.126371\n",
+              "min       4916.780000\n",
+              "25%       9160.390000\n",
+              "50%      10929.400000\n",
+              "75%      28889.610000\n",
+              "max      63558.480000\n",
+              "Name: close, dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8mK8uToX7uKJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 392
+        },
+        "outputId": "51c951a7-cfbf-47ce-b2e2-c932eda94e5c"
+      },
+      "source": [
+        "plt.rcParams['figure.figsize'] = [10,6]\n",
+        "plt.plot(target_col)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7f518cd31e90>]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KPzobcXnqlyq"
+      },
+      "source": [
+        "from sklearn.preprocessing import MinMaxScaler\n",
+        "scaler=MinMaxScaler(feature_range=(0,1))\n",
+        "target_col=scaler.fit_transform(np.array(target_col).reshape(-1,1))"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3ez8_5IvogUP"
+      },
+      "source": [
+        "##splitting dataset into train and test split\n",
+        "training_size=int(len(target_col)*0.80)\n",
+        "test_size=len(target_col)-training_size\n",
+        "train_data,test_data=target_col[0:training_size,:],target_col[training_size:len(target_col),:1]"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "FMon8VWLr6jO"
+      },
+      "source": [
+        "# convert an array of values into a dataset matrix\n",
+        "def create_dataset(dataset, time_step=1):\n",
+        "\tdataX, dataY = [], []\n",
+        "\tfor i in range(len(dataset)-time_step-1):\n",
+        "\t\ta = dataset[i:(i+time_step), 0]   ###i=0, 0,1,2,3-----99   100 \n",
+        "\t\tdataX.append(a)\n",
+        "\t\tdataY.append(dataset[i + time_step, 0])\n",
+        "\treturn np.array(dataX), np.array(dataY)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "583kizUCsA7u"
+      },
+      "source": [
+        "# reshape into X=t,t+1,t+2,t+3 and Y=t+4\n",
+        "time_step=1\n",
+        "X_train, y_train = create_dataset(train_data, time_step)\n",
+        "X_test, ytest = create_dataset(test_data, time_step)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ePpTIUHOkAt3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9700ea2a-1782-4b5b-fc1a-e87a0a881a7e"
+      },
+      "source": [
+        "print(X_train.shape), print(y_train.shape)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(398, 1)\n",
+            "(398,)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(None, None)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CNaEHZOwkIj3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "33081e98-08c2-44e9-f78b-09776dd0fc6f"
+      },
+      "source": [
+        "print(X_test.shape), print(ytest.shape)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "(99, 1)\n",
+            "(99,)\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(None, None)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fJ-XaSzikOqB"
+      },
+      "source": [
+        "# reshape input to be [samples, time steps, features] which is required for LSTM\n",
+        "X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)\n",
+        "X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NVGFeeABkYCl"
+      },
+      "source": [
+        "model=keras.Sequential()\n",
+        "model.add(LSTM(500,return_sequences=True,input_shape=(X_train.shape[1], X_train.shape[2])))\n",
+        "model.add(LSTM(500,return_sequences=True))\n",
+        "model.add(LSTM(500,return_sequences=False))\n",
+        "model.add(Dropout(0.2))\n",
+        "model.add(Activation('sigmoid'))\n",
+        "model.add(Dense(1))\n",
+        "#opt = keras.optimizers.Adam(learning_rate=0.01)\n",
+        "#model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=['mse'])\n",
+        "model.compile(loss='mean_squared_error',optimizer='adam', metrics=['mae'])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "xF8-LcpsqMt6",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5ef342e9-a93f-4a70-9316-72d3a0938303"
+      },
+      "source": [
+        "model.summary()"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Model: \"sequential\"\n",
+            "_________________________________________________________________\n",
+            "Layer (type)                 Output Shape              Param #   \n",
+            "=================================================================\n",
+            "lstm (LSTM)                  (None, 1, 500)            1004000   \n",
+            "_________________________________________________________________\n",
+            "lstm_1 (LSTM)                (None, 1, 500)            2002000   \n",
+            "_________________________________________________________________\n",
+            "lstm_2 (LSTM)                (None, 500)               2002000   \n",
+            "_________________________________________________________________\n",
+            "dropout (Dropout)            (None, 500)               0         \n",
+            "_________________________________________________________________\n",
+            "activation (Activation)      (None, 500)               0         \n",
+            "_________________________________________________________________\n",
+            "dense (Dense)                (None, 1)                 501       \n",
+            "=================================================================\n",
+            "Total params: 5,008,501\n",
+            "Trainable params: 5,008,501\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "oLH_xT7qqdpT",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "446bc1f0-ddc3-4a52-c174-15ea63bf63a8"
+      },
+      "source": [
+        "history=model.fit(X_train,y_train,validation_data=(X_test,ytest),epochs=20,batch_size=32,verbose=1)"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Epoch 1/20\n",
+            "13/13 [==============================] - 6s 162ms/step - loss: 0.0422 - mae: 0.1598 - val_loss: 0.3443 - val_mae: 0.5704\n",
+            "Epoch 2/20\n",
+            "13/13 [==============================] - 1s 42ms/step - loss: 0.0216 - mae: 0.1215 - val_loss: 0.4032 - val_mae: 0.6202\n",
+            "Epoch 3/20\n",
+            "13/13 [==============================] - 1s 42ms/step - loss: 0.0168 - mae: 0.0962 - val_loss: 0.3642 - val_mae: 0.5901\n",
+            "Epoch 4/20\n",
+            "13/13 [==============================] - 1s 42ms/step - loss: 0.0096 - mae: 0.0663 - val_loss: 0.1141 - val_mae: 0.3296\n",
+            "Epoch 5/20\n",
+            "13/13 [==============================] - 1s 41ms/step - loss: 0.0023 - mae: 0.0315 - val_loss: 0.0232 - val_mae: 0.1477\n",
+            "Epoch 6/20\n",
+            "13/13 [==============================] - 1s 43ms/step - loss: 0.0011 - mae: 0.0186 - val_loss: 0.0129 - val_mae: 0.1058\n",
+            "Epoch 7/20\n",
+            "13/13 [==============================] - 1s 44ms/step - loss: 3.7233e-04 - mae: 0.0123 - val_loss: 0.0049 - val_mae: 0.0604\n",
+            "Epoch 8/20\n",
+            "13/13 [==============================] - 1s 45ms/step - loss: 1.6180e-04 - mae: 0.0084 - val_loss: 0.0041 - val_mae: 0.0538\n",
+            "Epoch 9/20\n",
+            "13/13 [==============================] - 1s 43ms/step - loss: 1.6337e-04 - mae: 0.0091 - val_loss: 0.0064 - val_mae: 0.0707\n",
+            "Epoch 10/20\n",
+            "13/13 [==============================] - 1s 45ms/step - loss: 1.9074e-04 - mae: 0.0077 - val_loss: 0.0035 - val_mae: 0.0487\n",
+            "Epoch 11/20\n",
+            "13/13 [==============================] - 1s 41ms/step - loss: 1.1686e-04 - mae: 0.0067 - val_loss: 0.0044 - val_mae: 0.0561\n",
+            "Epoch 12/20\n",
+            "13/13 [==============================] - 1s 41ms/step - loss: 1.7812e-04 - mae: 0.0083 - val_loss: 0.0033 - val_mae: 0.0474\n",
+            "Epoch 13/20\n",
+            "13/13 [==============================] - 1s 42ms/step - loss: 1.6930e-04 - mae: 0.0077 - val_loss: 0.0055 - val_mae: 0.0643\n",
+            "Epoch 14/20\n",
+            "13/13 [==============================] - 1s 42ms/step - loss: 1.1692e-04 - mae: 0.0069 - val_loss: 0.0028 - val_mae: 0.0425\n",
+            "Epoch 15/20\n",
+            "13/13 [==============================] - 1s 42ms/step - loss: 1.2750e-04 - mae: 0.0074 - val_loss: 0.0043 - val_mae: 0.0553\n",
+            "Epoch 16/20\n",
+            "13/13 [==============================] - 1s 41ms/step - loss: 1.9787e-04 - mae: 0.0098 - val_loss: 0.0059 - val_mae: 0.0675\n",
+            "Epoch 17/20\n",
+            "13/13 [==============================] - 1s 41ms/step - loss: 2.1901e-04 - mae: 0.0115 - val_loss: 0.0030 - val_mae: 0.0441\n",
+            "Epoch 18/20\n",
+            "13/13 [==============================] - 1s 42ms/step - loss: 1.0347e-04 - mae: 0.0061 - val_loss: 0.0027 - val_mae: 0.0419\n",
+            "Epoch 19/20\n",
+            "13/13 [==============================] - 1s 41ms/step - loss: 1.2707e-04 - mae: 0.0068 - val_loss: 0.0032 - val_mae: 0.0466\n",
+            "Epoch 20/20\n",
+            "13/13 [==============================] - 1s 42ms/step - loss: 1.6478e-04 - mae: 0.0088 - val_loss: 0.0037 - val_mae: 0.0503\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2-RCPm8wjBUb",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bdbb3f64-e100-4436-d95d-169bc0e13d50"
+      },
+      "source": [
+        "history.history['loss']"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[0.03191959112882614,\n",
+              " 0.018243372440338135,\n",
+              " 0.014717623591423035,\n",
+              " 0.009163795970380306,\n",
+              " 0.0015256005572155118,\n",
+              " 0.0005994651583023369,\n",
+              " 0.0002838505315594375,\n",
+              " 0.00020431107259355485,\n",
+              " 0.00016604272241238505,\n",
+              " 0.00015689247811678797,\n",
+              " 0.00014251841639634222,\n",
+              " 0.00016275570669677109,\n",
+              " 0.000160191222676076,\n",
+              " 0.00011929321772186086,\n",
+              " 0.0001646284363232553,\n",
+              " 0.000203924355446361,\n",
+              " 0.00019159115618094802,\n",
+              " 0.0001174519638880156,\n",
+              " 0.00013711779320146888,\n",
+              " 0.00019837942090816796]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "-Rkxen9OBHSw",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 616
+        },
+        "outputId": "f83471ee-7644-47b1-8fea-f271a09eb04a"
+      },
+      "source": [
+        "trace1 = go.Scatter(\n",
+        "    x = np.arange(0, len(history.history['loss']), 1),\n",
+        "    y = history.history['loss'],\n",
+        "    mode = 'lines',\n",
+        "    name = 'Train loss',\n",
+        "    line = dict(color=('rgb(255,0,0)'), width=2, dash='dash')\n",
+        ")\n",
+        "trace2 = go.Scatter(\n",
+        "    x = np.arange(0, len(history.history['val_loss']), 1),\n",
+        "    y = history.history['val_loss'],\n",
+        "    mode = 'lines',\n",
+        "    name = 'Test loss',\n",
+        "    line = dict(color=('rgb(1, 0, 255)'), width=2)\n",
+        ")\n",
+        "\n",
+        "data = [trace1, trace2]\n",
+        "layout = go.Layout(\n",
+        "    autosize=False,\n",
+        "    width=750,\n",
+        "    height=600,\n",
+        "\n",
+        "    title = 'Train and Test Loss during training',\n",
+        "    xaxis= go.layout.XAxis(linecolor = 'black',\n",
+        "                          linewidth = 1,\n",
+        "                          mirror = True,\n",
+        "                          title = \"Epoch number\",\n",
+        "                          color = 'black'\n",
+        "                          ),\n",
+        "\n",
+        "    yaxis= go.layout.YAxis(linecolor = 'red',\n",
+        "                          linewidth = 1,\n",
+        "                          mirror = True,\n",
+        "                          title = \"Loss\",\n",
+        "                           color = 'black'),\n",
+        "\n",
+        "    margin=go.layout.Margin(\n",
+        "        l=50,\n",
+        "        r=50,\n",
+        "        b=100,\n",
+        "        t=100,\n",
+        "        pad = 4\n",
+        "    )\n",
+        ")\n",
+        "\n",
+        "fig = go.Figure(data=data, layout=layout)\n",
+        "py.iplot(fig, filename='training_process')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
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+              "var x = new MutationObserver(function (mutations, observer) {{\n",
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+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })\n",
+              "                };\n",
+              "                \n",
+              "            </script>\n",
+              "        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wt88cq4-CE4C",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 616
+        },
+        "outputId": "bc8520e0-71b8-42f3-9d7e-f9f340911ba2"
+      },
+      "source": [
+        "trace1 = go.Scatter(\n",
+        "    x = np.arange(0, len(history.history['mae']), 1),\n",
+        "    y = history.history['mae'],\n",
+        "    mode = 'lines',\n",
+        "    name = 'Train Mean Absolute Error',\n",
+        "    line = dict(color=('rgb(0,0,139)'), width=2, dash='dash')\n",
+        ")\n",
+        "trace2 = go.Scatter(\n",
+        "    x = np.arange(0, len(history.history['val_mae']), 1),\n",
+        "    y = history.history['val_mae'],\n",
+        "    mode = 'lines',\n",
+        "    name = 'Test Mean Absolute Error',\n",
+        "    line = dict(color=('rgb(1, 0, 0)'), width=2)\n",
+        ")\n",
+        "\n",
+        "data = [trace1, trace2]\n",
+        "layout = go.Layout(\n",
+        "    autosize=False,\n",
+        "    width=750,\n",
+        "    height=600,\n",
+        "\n",
+        "    title = 'Train and Test Mean Squared Error during training',\n",
+        "    xaxis= go.layout.XAxis(linecolor = 'black',\n",
+        "                          linewidth = 1,\n",
+        "                          mirror = True,\n",
+        "                          title = \"Epoch number\",\n",
+        "                          color = 'black'\n",
+        "                          ),\n",
+        "\n",
+        "    yaxis= go.layout.YAxis(linecolor = 'red',\n",
+        "                          linewidth = 1,\n",
+        "                          mirror = True,\n",
+        "                          title = \"Mean Absolute Error\",\n",
+        "                           color = 'black'),\n",
+        "\n",
+        "    margin=go.layout.Margin(\n",
+        "        l=50,\n",
+        "        r=50,\n",
+        "        b=100,\n",
+        "        t=100,\n",
+        "        pad = 4\n",
+        "    )\n",
+        ")\n",
+        "\n",
+        "fig = go.Figure(data=data, layout=layout)\n",
+        "py.iplot(fig, filename='training_process')"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>\n",
+              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
+              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
+              "            <div id=\"6111f917-251c-4833-a48a-d2fe25d0ffff\" class=\"plotly-graph-div\" style=\"height:600px; width:750px;\"></div>\n",
+              "            <script type=\"text/javascript\">\n",
+              "                \n",
+              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
+              "                    \n",
+              "                if (document.getElementById(\"6111f917-251c-4833-a48a-d2fe25d0ffff\")) {\n",
+              "                    Plotly.newPlot(\n",
+              "                        '6111f917-251c-4833-a48a-d2fe25d0ffff',\n",
+              "                        [{\"line\": {\"color\": \"rgb(0,0,139)\", \"dash\": \"dash\", \"width\": 2}, \"mode\": \"lines\", \"name\": \"Train Mean Absolute Error\", \"type\": \"scatter\", \"x\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], \"y\": [0.13227638602256775, 0.09827647358179092, 0.08509460836648941, 0.06485840678215027, 0.02389504760503769, 0.014748598448932171, 0.010887573473155499, 0.008769395761191845, 0.008869457058608532, 0.007498753722757101, 0.00709223048761487, 0.00829450972378254, 0.007124415133148432, 0.006769220344722271, 0.0081796208396554, 0.010284418240189552, 0.010304966941475868, 0.00639445660635829, 0.006888981442898512, 0.010113021358847618]}, {\"line\": {\"color\": \"rgb(1, 0, 0)\", \"width\": 2}, \"mode\": \"lines\", \"name\": \"Test Mean Absolute Error\", \"type\": \"scatter\", \"x\": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], \"y\": [0.5704196691513062, 0.6201988458633423, 0.590086042881012, 0.32956239581108093, 0.14767205715179443, 0.10583365708589554, 0.06040019169449806, 0.053754955530166626, 0.07072297483682632, 0.04867550730705261, 0.05613739416003227, 0.04735608398914337, 0.06426912546157837, 0.042503803968429565, 0.055320776998996735, 0.06749662756919861, 0.044106725603342056, 0.04187443107366562, 0.04664134979248047, 0.05027846246957779]}],\n",
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\"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Train and Test Mean Squared Error during training\"}, \"width\": 750, \"xaxis\": {\"color\": \"black\", \"linecolor\": \"black\", \"linewidth\": 1, \"mirror\": true, \"title\": {\"text\": \"Epoch number\"}}, \"yaxis\": {\"color\": \"black\", \"linecolor\": \"red\", \"linewidth\": 1, \"mirror\": true, \"title\": {\"text\": \"Mean Absolute Error\"}}},\n",
+              "                        {\"responsive\": true}\n",
+              "                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('6111f917-251c-4833-a48a-d2fe25d0ffff');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })\n",
+              "                };\n",
+              "                \n",
+              "            </script>\n",
+              "        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jgbBKA6NClxf"
+      },
+      "source": [
+        "# add one additional data point to align shapes of the predictions and true labels\n",
+        "#X_test = np.append(X_test, scaler.transform(data.iloc[-1][0]))\n",
+        "#X_test = np.reshape(X_test, (len(X_test), 1, 1))\n",
+        "\n",
+        "# get predictions and then make some transformations to be able to calculate RMSE properly in USD\n",
+        "prediction = model.predict(X_test)\n",
+        "prediction_inverse = scaler.inverse_transform(prediction.reshape(-1, 1))\n",
+        "Y_test_inverse = scaler.inverse_transform(ytest.reshape(-1, 1))\n",
+        "prediction2_inverse = np.array(prediction_inverse[:,0][1:])\n",
+        "Y_test2_inverse = np.array(Y_test_inverse[:,0])"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "m8SXDEzbDPzM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f8613de7-48ba-4dc6-d1b1-618cca8b5402"
+      },
+      "source": [
+        "trace1 = go.Scatter(\n",
+        "    x = np.arange(0, len(prediction2_inverse), 1),\n",
+        "    y = prediction2_inverse,\n",
+        "    mode = 'lines',\n",
+        "    name = 'Predicted Labels',\n",
+        "    line = dict(color=('rgb(0,0,139)'), width=2)\n",
+        ")\n",
+        "trace2 = go.Scatter(\n",
+        "    x = np.arange(0, len(Y_test2_inverse), 1),\n",
+        "    y = Y_test2_inverse,\n",
+        "    mode = 'lines',\n",
+        "    name = 'True labels',\n",
+        "    line = dict(color=('rgb(255, 0, 0)'), width=2)\n",
+        "\n",
+        ")\n",
+        "data = [trace1, trace2]\n",
+        "\n",
+        "layout = go.Layout(\n",
+        "    autosize=False,\n",
+        "    width=750,\n",
+        "    height=600,\n",
+        "\n",
+        "    title = 'Prediction Graph',\n",
+        "    xaxis= go.layout.XAxis(linecolor = 'black',\n",
+        "                          linewidth = 1,\n",
+        "                          mirror = True,\n",
+        "                          title = \"Number of days\",\n",
+        "                          color = 'black'\n",
+        "                          ),                          \n",
+        "\n",
+        "    yaxis= go.layout.YAxis(linecolor = 'red',\n",
+        "                          linewidth = 1,\n",
+        "                          mirror = True,\n",
+        "                          title = \"Price in USD\",\n",
+        "                           color = 'black'),\n",
+        "\n",
+        "    margin=go.layout.Margin(\n",
+        "        l=50,\n",
+        "        r=50,\n",
+        "        b=100,\n",
+        "        t=100,\n",
+        "        pad = 4\n",
+        "    )\n",
+        ")\n",
+        "\n",
+        "fig = go.Figure(data=data, layout=layout)\n",
+        "py.iplot(fig, filename='results_demonstrating0')\n",
+        "plt.savefig('demonstrating0.png', dpi=300, transparent=False, bbox_inches='tight')\n"
+      ],
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>\n",
+              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
+              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
+              "            <div id=\"a79c7a85-6d21-421d-a360-a708d2b9e71f\" class=\"plotly-graph-div\" style=\"height:600px; width:750px;\"></div>\n",
+              "            <script type=\"text/javascript\">\n",
+              "                \n",
+              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
+              "                    \n",
+              "                if (document.getElementById(\"a79c7a85-6d21-421d-a360-a708d2b9e71f\")) {\n",
+              "                    Plotly.newPlot(\n",
+              "                        'a79c7a85-6d21-421d-a360-a708d2b9e71f',\n",
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+              "                        {\"responsive\": true}\n",
+              "                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('a79c7a85-6d21-421d-a360-a708d2b9e71f');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })\n",
+              "                };\n",
+              "                \n",
+              "            </script>\n",
+              "        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x432 with 0 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file

From 514a8a281e5490207c28c42a9dafe8579647f657 Mon Sep 17 00:00:00 2001
From: Ammar Ayoubi <59621670+ammarayoubi@users.noreply.github.com>
Date: Tue, 26 Oct 2021 21:41:35 +0500
Subject: [PATCH 101/232] Rename Dart program to check if a number is prime or
 not. to Dart program to check if a number is prime or not.dart

output:
Please Enter a number: 7
Given number is prime number
---
 ...ot. => Dart program to check if a number is prime or not.dart} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Dart/{Dart program to check if a number is prime or not. => Dart program to check if a number is prime or not.dart} (100%)

diff --git a/Dart/Dart program to check if a number is prime or not. b/Dart/Dart program to check if a number is prime or not.dart
similarity index 100%
rename from Dart/Dart program to check if a number is prime or not.
rename to Dart/Dart program to check if a number is prime or not.dart

From 321bc24f7d3005e0f30a629c70bc14fbe88d7870 Mon Sep 17 00:00:00 2001
From: Ammar Ayoubi <59621670+ammarayoubi@users.noreply.github.com>
Date: Tue, 26 Oct 2021 21:49:48 +0500
Subject: [PATCH 102/232] Rename Dart program to check if a string is
 palindrome to Dart program to check if a string is palindrome.dart

---
 ...drome => Dart program to check if a string is palindrome.dart} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Dart/{Dart program to check if a string is palindrome => Dart program to check if a string is palindrome.dart} (100%)

diff --git a/Dart/Dart program to check if a string is palindrome b/Dart/Dart program to check if a string is palindrome.dart
similarity index 100%
rename from Dart/Dart program to check if a string is palindrome
rename to Dart/Dart program to check if a string is palindrome.dart

From 2b27a20d23f55282b0f0355607a21fe0ee554259 Mon Sep 17 00:00:00 2001
From: Ammar Ayoubi <59621670+ammarayoubi@users.noreply.github.com>
Date: Tue, 26 Oct 2021 21:53:23 +0500
Subject: [PATCH 103/232] Rename Dart program to check if a number is prime or
 not. to Dart program to check if a number is prime or not.dart

---
 ...ot. => Dart program to check if a number is prime or not.dart} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Dart/{Dart program to check if a number is prime or not. => Dart program to check if a number is prime or not.dart} (100%)

diff --git a/Dart/Dart program to check if a number is prime or not. b/Dart/Dart program to check if a number is prime or not.dart
similarity index 100%
rename from Dart/Dart program to check if a number is prime or not.
rename to Dart/Dart program to check if a number is prime or not.dart

From 826068e004aadf339bca1b3eea7c23d11d9d5d08 Mon Sep 17 00:00:00 2001
From: Ammar Ayoubi <59621670+ammarayoubi@users.noreply.github.com>
Date: Tue, 26 Oct 2021 21:54:40 +0500
Subject: [PATCH 104/232] Rename Dart program to upload file to firebase
 storage. to Dart program to upload file to firebase storage.dart

---
 ...rage. => Dart program to upload file to firebase storage.dart} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Dart/{Dart program to upload file to firebase storage. => Dart program to upload file to firebase storage.dart} (100%)

diff --git a/Dart/Dart program to upload file to firebase storage. b/Dart/Dart program to upload file to firebase storage.dart
similarity index 100%
rename from Dart/Dart program to upload file to firebase storage.
rename to Dart/Dart program to upload file to firebase storage.dart

From 561ce76d99546762bf16aab95b86eb38b5bc6f1e Mon Sep 17 00:00:00 2001
From: Ammar Ayoubi <59621670+ammarayoubi@users.noreply.github.com>
Date: Tue, 26 Oct 2021 21:57:20 +0500
Subject: [PATCH 105/232] Rename Dart program to compress image to Dart program
 to compress image.dart

---
 ...gram to compress image => Dart program to compress image.dart} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Dart/{Dart program to compress image => Dart program to compress image.dart} (100%)

diff --git a/Dart/Dart program to compress image b/Dart/Dart program to compress image.dart
similarity index 100%
rename from Dart/Dart program to compress image
rename to Dart/Dart program to compress image.dart

From 7bd11c38a9ff03531ca01d736c53f0be1ef8112c Mon Sep 17 00:00:00 2001
From: Kainat-Mahar <72600530+Kainat-Mahar@users.noreply.github.com>
Date: Wed, 27 Oct 2021 14:33:06 +0500
Subject: [PATCH 106/232] Add files via upload

---
 ...E PREDICTION USING LSTM NEURAL NETWORK.docx | Bin 0 -> 542850 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Machine Learning/BITCOIN PRICE PREDICTION USING LSTM NEURAL NETWORK.docx

diff --git a/Machine Learning/BITCOIN PRICE PREDICTION USING LSTM NEURAL NETWORK.docx b/Machine Learning/BITCOIN PRICE PREDICTION USING LSTM NEURAL NETWORK.docx
new file mode 100644
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T31?V<1pG1g#|o3g{v7-Zd?i21

literal 0
HcmV?d00001


From 3b704c5cda3b03de376248440170f766372ea2f6 Mon Sep 17 00:00:00 2001
From: HumbalKK <45451264+HumbalKK@users.noreply.github.com>
Date: Wed, 27 Oct 2021 16:01:54 +0500
Subject: [PATCH 107/232] Create Rolling a Dice

Example of random function how can we call a random function an example of rolling a dice choosing a random number from a given list
---
 Python/Rolling a Dice | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 Python/Rolling a Dice

diff --git a/Python/Rolling a Dice b/Python/Rolling a Dice
new file mode 100644
index 0000000..06b4cd0
--- /dev/null
+++ b/Python/Rolling a Dice	
@@ -0,0 +1,8 @@
+# importing random
+import random
+
+# prints a random value from the list
+
+list1 = [1, 2, 3, 4, 5, 6]
+
+print(random.choice(list1))

From 6fde915f3ece82e95e26fd7969773d9e71f810c0 Mon Sep 17 00:00:00 2001
From: HumbalKK <45451264+HumbalKK@users.noreply.github.com>
Date: Wed, 27 Oct 2021 23:37:09 +0500
Subject: [PATCH 108/232] Create Example of regrassion

a simple code explaining regression
---
 Python/Example of regrassion | 12 ++++++++++++
 1 file changed, 12 insertions(+)
 create mode 100644 Python/Example of regrassion

diff --git a/Python/Example of regrassion b/Python/Example of regrassion
new file mode 100644
index 0000000..5a66655
--- /dev/null
+++ b/Python/Example of regrassion	
@@ -0,0 +1,12 @@
+import re
+
+line = "Cats are smarter than dogs"
+
+matchObj = re.match( r'(.*) are (.*?) .*', line, re.M|re.I)
+
+if matchObj:
+   print ("matchObj.group() : ", matchObj.group())
+   print ("matchObj.group(1) : ", matchObj.group(1))
+   print ("matchObj.group(2) : ", matchObj.group(2))
+else:
+   print ("No match!!")

From 8c8a0caf62358a7ff7c874328d20b865dc899c29 Mon Sep 17 00:00:00 2001
From: HumbalKK <45451264+HumbalKK@users.noreply.github.com>
Date: Wed, 27 Oct 2021 23:37:46 +0500
Subject: [PATCH 109/232] Create Example of regrassion

a simple code explaining regression
---
 Python/Example of regrassion | 12 ++++++++++++
 1 file changed, 12 insertions(+)
 create mode 100644 Python/Example of regrassion

diff --git a/Python/Example of regrassion b/Python/Example of regrassion
new file mode 100644
index 0000000..5a66655
--- /dev/null
+++ b/Python/Example of regrassion	
@@ -0,0 +1,12 @@
+import re
+
+line = "Cats are smarter than dogs"
+
+matchObj = re.match( r'(.*) are (.*?) .*', line, re.M|re.I)
+
+if matchObj:
+   print ("matchObj.group() : ", matchObj.group())
+   print ("matchObj.group(1) : ", matchObj.group(1))
+   print ("matchObj.group(2) : ", matchObj.group(2))
+else:
+   print ("No match!!")

From 651faf07955cb86cf05064ea7fc7ad118b4501aa Mon Sep 17 00:00:00 2001
From: HumbalKK <45451264+HumbalKK@users.noreply.github.com>
Date: Wed, 27 Oct 2021 23:41:50 +0500
Subject: [PATCH 110/232] Create Example of Threading

2 threads are created and there relative output is observed
---
 Python/Example of Threading | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)
 create mode 100644 Python/Example of Threading

diff --git a/Python/Example of Threading b/Python/Example of Threading
new file mode 100644
index 0000000..50882d5
--- /dev/null
+++ b/Python/Example of Threading	
@@ -0,0 +1,20 @@
+import _thread
+import time
+
+# Define a function for the thread
+def print_time( threadName, delay):
+   count = 0
+   while count < 5:
+      time.sleep(delay)
+      count += 1
+      print ("%s: %s" % ( threadName, time.ctime(time.time()) ))
+
+# Create two threads as follows
+try:
+   _thread.start_new_thread( print_time, ("Thread-1", 2, ) )
+   _thread.start_new_thread( print_time, ("Thread-2", 4, ) )
+except:
+   print ("Error: unable to start thread")
+
+while 1:
+   pass

From 9701aae2ee6bed1bcfd63ac1f41d1cd9b590f1db Mon Sep 17 00:00:00 2001
From: HumbalKK <45451264+HumbalKK@users.noreply.github.com>
Date: Wed, 27 Oct 2021 23:47:03 +0500
Subject: [PATCH 111/232] Create Example to connect MySQL data base

Example to connect DB and fetch data (version) from database
---
 Python/Example to connect MySQL data base | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)
 create mode 100644 Python/Example to connect MySQL data base

diff --git a/Python/Example to connect MySQL data base b/Python/Example to connect MySQL data base
new file mode 100644
index 0000000..c860409
--- /dev/null
+++ b/Python/Example to connect MySQL data base	
@@ -0,0 +1,17 @@
+import pymysql  # first install pymysql using pip. here we inport pymysql to connect DB
+
+# Open database connection, test data base connectivity is being established 
+db = pymysql.connect("localhost","testuser","test123","TESTDB" )
+
+# prepare a cursor object using cursor() method
+cursor = db.cursor()
+
+# execute SQL query using execute() method.
+cursor.execute("SELECT VERSION()")
+
+# Fetch a single row using fetchone() method.
+data = cursor.fetchone()
+print ("Database version : %s " % data)
+
+# disconnect from server
+db.close()

From 5cd46d172687f38bf691ff86ef2b68a9e172a3f6 Mon Sep 17 00:00:00 2001
From: HumbalKK <45451264+HumbalKK@users.noreply.github.com>
Date: Thu, 28 Oct 2021 00:02:12 +0500
Subject: [PATCH 112/232] Create Example for Exceptional Handling

here is an example for exceptional handling this piece of code will open a file write data to it and close it
there are 2 options
1. file found and task completed
2. file not found or read-only file

so instead of a program crash or compilation error we use exceptions to avoid program termination
---
 Python/Example for Exceptional Handling | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 Python/Example for Exceptional Handling

diff --git a/Python/Example for Exceptional Handling b/Python/Example for Exceptional Handling
new file mode 100644
index 0000000..29b4531
--- /dev/null
+++ b/Python/Example for Exceptional Handling	
@@ -0,0 +1,8 @@
+try:
+   fh = open("testfile", "w")
+   fh.write("This is my test file for exception handling!!")
+except IOError:
+   print ("Error: can\'t find file or read data")
+else:
+   print ("Written content in the file successfully")
+   fh.close()

From 27612aefae07a7d9d644eed640b574d722172a6d Mon Sep 17 00:00:00 2001
From: eqrakhattak <eqra.khattak7@gmail.com>
Date: Thu, 28 Oct 2021 02:39:40 +0500
Subject: [PATCH 113/232] Add eqra to contributors list

---
 Python/list with differnt items.py | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 Python/list with differnt items.py

diff --git a/Python/list with differnt items.py b/Python/list with differnt items.py
new file mode 100644
index 0000000..87fac45
--- /dev/null
+++ b/Python/list with differnt items.py	
@@ -0,0 +1,8 @@
+print("Enter different types of inputs in the list")
+
+lst=[]
+for i in range(0,5):
+    n = input()
+    lst.append(n)
+
+print(lst)

From 2ecbe069d2804568a437942625e5d0a353396f2c Mon Sep 17 00:00:00 2001
From: MuhammadRafayKhan <31164804+MuhammadRafayKhan@users.noreply.github.com>
Date: Thu, 28 Oct 2021 12:47:21 +0500
Subject: [PATCH 114/232] Depth First Traversal

Depth First Traversal
---
 Python/graphs/DepthFirstTraversal | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 Python/graphs/DepthFirstTraversal

diff --git a/Python/graphs/DepthFirstTraversal b/Python/graphs/DepthFirstTraversal
new file mode 100644
index 0000000..617f0cd
--- /dev/null
+++ b/Python/graphs/DepthFirstTraversal
@@ -0,0 +1,23 @@
+class graph:
+   def __init__(self,gdict=None):
+      if gdict is None:
+         gdict = {}
+      self.gdict = gdict
+# Check for the visisted and unvisited nodes
+def dfs(graph, start, visited = None):
+   if visited is None:
+      visited = set()
+   visited.add(start)
+   print(start)
+   for next in graph[start] - visited:
+      dfs(graph, next, visited)
+   return visited
+
+gdict = { 
+   "a" : set(["b","c"]),
+   "b" : set(["a", "d"]),
+   "c" : set(["a", "d"]),
+   "d" : set(["e"]),
+   "e" : set(["a"])
+}
+dfs(gdict, 'a')

From 5bc41ca50c66d6b12533d64ec1a4b347d3fa6bfd Mon Sep 17 00:00:00 2001
From: MuhammadRafayKhan <31164804+MuhammadRafayKhan@users.noreply.github.com>
Date: Thu, 28 Oct 2021 12:49:46 +0500
Subject: [PATCH 115/232] Rename DepthFirstTraversal to DepthFirstTraversal.py

---
 Python/graphs/{DepthFirstTraversal => DepthFirstTraversal.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Python/graphs/{DepthFirstTraversal => DepthFirstTraversal.py} (100%)

diff --git a/Python/graphs/DepthFirstTraversal b/Python/graphs/DepthFirstTraversal.py
similarity index 100%
rename from Python/graphs/DepthFirstTraversal
rename to Python/graphs/DepthFirstTraversal.py

From a6463eee8a6fcd9a1be02da9a1c88350f0b8117a Mon Sep 17 00:00:00 2001
From: MuhammadRafayKhan <31164804+MuhammadRafayKhan@users.noreply.github.com>
Date: Thu, 28 Oct 2021 12:51:36 +0500
Subject: [PATCH 116/232] Breadth First Traversal

Breadth First Traversal
---
 Python/graphs/BreadthFirstTraversal.py | 29 ++++++++++++++++++++++++++
 1 file changed, 29 insertions(+)
 create mode 100644 Python/graphs/BreadthFirstTraversal.py

diff --git a/Python/graphs/BreadthFirstTraversal.py b/Python/graphs/BreadthFirstTraversal.py
new file mode 100644
index 0000000..c630d01
--- /dev/null
+++ b/Python/graphs/BreadthFirstTraversal.py
@@ -0,0 +1,29 @@
+import collections
+class graph:
+   def __init__(self,gdict=None):
+      if gdict is None:
+         gdict = {}
+      self.gdict = gdict
+def bfs(graph, startnode):
+# Track the visited and unvisited nodes using queue
+   seen, queue = set([startnode]), collections.deque([startnode])
+   while queue:
+      vertex = queue.popleft()
+      marked(vertex)
+      for node in graph[vertex]:
+         if node not in seen:
+            seen.add(node)
+            queue.append(node)
+
+def marked(n):
+   print(n)
+
+# The graph dictionary
+gdict = { 
+   "a" : set(["b","c"]),
+   "b" : set(["a", "d"]),
+   "c" : set(["a", "d"]),
+   "d" : set(["e"]),
+   "e" : set(["a"])
+}
+bfs(gdict, "a")

From acf6b75b69be37b4aba30f115a87445a49acf4d6 Mon Sep 17 00:00:00 2001
From: MuhammadRafayKhan <31164804+MuhammadRafayKhan@users.noreply.github.com>
Date: Thu, 28 Oct 2021 12:54:05 +0500
Subject: [PATCH 117/232] Creating a Heap

Creating a Heap
---
 Python/heap/Creating.py | 6 ++++++
 1 file changed, 6 insertions(+)
 create mode 100644 Python/heap/Creating.py

diff --git a/Python/heap/Creating.py b/Python/heap/Creating.py
new file mode 100644
index 0000000..d0ed93d
--- /dev/null
+++ b/Python/heap/Creating.py
@@ -0,0 +1,6 @@
+import heapq
+
+H = [21,1,45,78,3,5]
+# Use heapify to rearrange the elements
+heapq.heapify(H)
+print(H)

From ddc221981083b2eb55ceecdaec39e504b35fd4c4 Mon Sep 17 00:00:00 2001
From: MuhammadRafayKhan <31164804+MuhammadRafayKhan@users.noreply.github.com>
Date: Thu, 28 Oct 2021 12:55:28 +0500
Subject: [PATCH 118/232] Inserting into heap

Inserting into heap
---
 Python/heap/Inserting.py | 10 ++++++++++
 1 file changed, 10 insertions(+)
 create mode 100644 Python/heap/Inserting.py

diff --git a/Python/heap/Inserting.py b/Python/heap/Inserting.py
new file mode 100644
index 0000000..635053a
--- /dev/null
+++ b/Python/heap/Inserting.py
@@ -0,0 +1,10 @@
+import heapq
+
+H = [21,1,45,78,3,5]
+# Covert to a heap
+heapq.heapify(H)
+print(H)
+
+# Add element
+heapq.heappush(H,8)
+print(H)

From 38a2b6f638411a641262e68e8d74fead6a77cf37 Mon Sep 17 00:00:00 2001
From: HumbalKK <45451264+HumbalKK@users.noreply.github.com>
Date: Thu, 28 Oct 2021 22:50:23 +0500
Subject: [PATCH 119/232] Rename Rolling a Dice to Rolling a Dice.py

---
 Python/{Rolling a Dice => Rolling a Dice.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Python/{Rolling a Dice => Rolling a Dice.py} (100%)

diff --git a/Python/Rolling a Dice b/Python/Rolling a Dice.py
similarity index 100%
rename from Python/Rolling a Dice
rename to Python/Rolling a Dice.py

From 108d2f0ad30181fb95596e36f799b0ad27fd37fb Mon Sep 17 00:00:00 2001
From: HumbalKK <45451264+HumbalKK@users.noreply.github.com>
Date: Thu, 28 Oct 2021 22:52:00 +0500
Subject: [PATCH 120/232] Rename Example of regrassion to Example of
 regression.py

---
 Python/{Example of regrassion => Example of regression.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Python/{Example of regrassion => Example of regression.py} (100%)

diff --git a/Python/Example of regrassion b/Python/Example of regression.py
similarity index 100%
rename from Python/Example of regrassion
rename to Python/Example of regression.py

From c9b9d3d4a585bb32213c2a4545c059d61678d024 Mon Sep 17 00:00:00 2001
From: HumbalKK <45451264+HumbalKK@users.noreply.github.com>
Date: Thu, 28 Oct 2021 22:53:29 +0500
Subject: [PATCH 121/232] Rename Example of Threading to Example of
 Threading.py

---
 Python/{Example of Threading => Example of Threading.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Python/{Example of Threading => Example of Threading.py} (100%)

diff --git a/Python/Example of Threading b/Python/Example of Threading.py
similarity index 100%
rename from Python/Example of Threading
rename to Python/Example of Threading.py

From cff35cc8b1aa81d442e749e38f0f79ae00c0e916 Mon Sep 17 00:00:00 2001
From: HumbalKK <45451264+HumbalKK@users.noreply.github.com>
Date: Thu, 28 Oct 2021 22:55:45 +0500
Subject: [PATCH 122/232] Rename Example of regrassion to Example of
 regrassion.py

---
 Python/{Example of regrassion => Example of regrassion.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Python/{Example of regrassion => Example of regrassion.py} (100%)

diff --git a/Python/Example of regrassion b/Python/Example of regrassion.py
similarity index 100%
rename from Python/Example of regrassion
rename to Python/Example of regrassion.py

From c66fec7ad56e4770e27ed2162607fea3091f3d91 Mon Sep 17 00:00:00 2001
From: HumbalKK <45451264+HumbalKK@users.noreply.github.com>
Date: Thu, 28 Oct 2021 23:29:36 +0500
Subject: [PATCH 123/232] Create Read_and_plot_CSV_Data.py

it's just an example code how we can read a CSV file and plot its data in a form of a histogram
---
 Python/Read_and_plot_CSV_Data.py | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)
 create mode 100644 Python/Read_and_plot_CSV_Data.py

diff --git a/Python/Read_and_plot_CSV_Data.py b/Python/Read_and_plot_CSV_Data.py
new file mode 100644
index 0000000..98862fb
--- /dev/null
+++ b/Python/Read_and_plot_CSV_Data.py
@@ -0,0 +1,19 @@
+import matplotlib.pyplot as plt   # here we are importing matplot library to plot data 
+import csv                        # here we imported CSV library to read CSV file 
+
+x = []                            # here we declared an empty array variable 
+y = []                            # here we declared an empty array variable
+
+with open('Example.csv','r') as csvfile:       # here we open CSV file 
+	plots = csv.reader(csvfile, delimiter = ',') # ploting csv file data 
+	
+	for row in plots:
+		x.append(row[0])                          # collecting data from rows and storing in array 
+		y.append(int(row[2]))
+
+plt.bar(x, y, color = 'g', width = 0.72, label = "Age")    #ploting data with Labels 
+plt.xlabel('Names')                                        # declaring Labels 
+plt.ylabel('Ages')                                         # declaring Labels
+plt.title('Ages of different persons')                     # declaring Title
+plt.legend()
+plt.show()

From 9bcc8653786c64ee7fb5dd0b3eab5f15abb462d3 Mon Sep 17 00:00:00 2001
From: HumbalKK <45451264+HumbalKK@users.noreply.github.com>
Date: Thu, 28 Oct 2021 23:31:17 +0500
Subject: [PATCH 124/232] Rename Example to connect MySQL data base to Example
 to connect MySQL data base.py

---
 ...nect MySQL data base => Example to connect MySQL data base.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Python/{Example to connect MySQL data base => Example to connect MySQL data base.py} (100%)

diff --git a/Python/Example to connect MySQL data base b/Python/Example to connect MySQL data base.py
similarity index 100%
rename from Python/Example to connect MySQL data base
rename to Python/Example to connect MySQL data base.py

From 41b3ef4a83ad90b70a4a12ae0b489e7aa6cd2205 Mon Sep 17 00:00:00 2001
From: HumbalKK <45451264+HumbalKK@users.noreply.github.com>
Date: Fri, 29 Oct 2021 13:18:36 +0500
Subject: [PATCH 125/232] Rename Example for Exceptional Handling to
 Exception_handling.py

---
 .../{Example for Exceptional Handling => Exception_handling.py}   | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Python/{Example for Exceptional Handling => Exception_handling.py} (100%)

diff --git a/Python/Example for Exceptional Handling b/Python/Exception_handling.py
similarity index 100%
rename from Python/Example for Exceptional Handling
rename to Python/Exception_handling.py

From 4d8a461589603e3fca7860e695dfe40a8a2c9cb9 Mon Sep 17 00:00:00 2001
From: HumbalKK <45451264+HumbalKK@users.noreply.github.com>
Date: Fri, 29 Oct 2021 13:20:24 +0500
Subject: [PATCH 126/232] Rename Example of Threading.py to
 Threading_Example.py

---
 Python/{Example of Threading.py => Threading_Example.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Python/{Example of Threading.py => Threading_Example.py} (100%)

diff --git a/Python/Example of Threading.py b/Python/Threading_Example.py
similarity index 100%
rename from Python/Example of Threading.py
rename to Python/Threading_Example.py

From c79c8b5fc6cdaf002f36d745fb0dcaa2458322fe Mon Sep 17 00:00:00 2001
From: HumbalKK <45451264+HumbalKK@users.noreply.github.com>
Date: Fri, 29 Oct 2021 13:21:17 +0500
Subject: [PATCH 127/232] Rename Rolling a Dice.py to Rolling_Dice.py

---
 Python/{Rolling a Dice.py => Rolling_Dice.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Python/{Rolling a Dice.py => Rolling_Dice.py} (100%)

diff --git a/Python/Rolling a Dice.py b/Python/Rolling_Dice.py
similarity index 100%
rename from Python/Rolling a Dice.py
rename to Python/Rolling_Dice.py

From 80927ffa64c3ee231ff45b9d5c7d390cac28de88 Mon Sep 17 00:00:00 2001
From: HumbalKK <45451264+HumbalKK@users.noreply.github.com>
Date: Fri, 29 Oct 2021 13:23:09 +0500
Subject: [PATCH 128/232] Rename Example of regression.py to
 Regression_Example.py

---
 Python/{Example of regression.py => Regression_Example.py} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Python/{Example of regression.py => Regression_Example.py} (100%)

diff --git a/Python/Example of regression.py b/Python/Regression_Example.py
similarity index 100%
rename from Python/Example of regression.py
rename to Python/Regression_Example.py

From 745a013e453c58a9793a87d5c9f40965449246cd Mon Sep 17 00:00:00 2001
From: MUHAMMAD KALEEM ULLAH <92902603+fa19bce007@users.noreply.github.com>
Date: Sat, 30 Oct 2021 18:31:26 +0500
Subject: [PATCH 129/232] Create User_defined_Exception.cs

---
 C#/User_defined_Exception.cs | 43 ++++++++++++++++++++++++++++++++++++
 1 file changed, 43 insertions(+)
 create mode 100644 C#/User_defined_Exception.cs

diff --git a/C#/User_defined_Exception.cs b/C#/User_defined_Exception.cs
new file mode 100644
index 0000000..9624aeb
--- /dev/null
+++ b/C#/User_defined_Exception.cs
@@ -0,0 +1,43 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+using System.Threading.Tasks;
+
+namespace userdefined_Exception
+{
+    class Program
+    {
+        static void Main(string[] args)
+        {
+            try
+            {
+                int a = CheckAge();
+                Console.WriteLine(a);
+                Console.ReadLine();
+            }
+            catch (UserDefinedEX_AGE ex)
+            {
+                Console.WriteLine(ex.Message);
+            }
+        }
+        static int CheckAge()
+        {
+            Console.WriteLine(" ---Enter AGE--- ");
+            int age = Convert.ToInt32(Console.ReadLine());
+            if (age <= 0)
+            {
+                throw new Exception("Age should be POSITIVE or at least Greater than 0");
+            }
+            return age;
+        }
+
+        public class UserDefinedEX_AGE : Exception
+        {
+            public UserDefinedEX_AGE(string msg) : base(msg)
+            {
+
+            }
+        }
+    }
+}

From 2e4e150fa26a78afe85a816faaea8eaf8a714488 Mon Sep 17 00:00:00 2001
From: Ahmednae <91714237+Ahmednae@users.noreply.github.com>
Date: Sun, 31 Oct 2021 02:18:57 +0500
Subject: [PATCH 130/232] Create square star pattern

---
 Javascript/square star pattern | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)
 create mode 100644 Javascript/square star pattern

diff --git a/Javascript/square star pattern b/Javascript/square star pattern
new file mode 100644
index 0000000..255457e
--- /dev/null
+++ b/Javascript/square star pattern	
@@ -0,0 +1,16 @@
+Run two nested for loops to create a square star pattern. Run each loop 'n' times, where 'n' is the number of rows/columns in the square, i.e. for(let i= 0;  i< n; i++).
+
+The internal loop will run for 'n' times, printing starts in a row and a newline at the end of the loop (n), whereas the outer loop will run the internal loop for 'n' times, printing starts in a column (n).
+let n = 5; // row or column count
+// defining an empty string
+let string = "";
+
+for(let i = 0; i < n; i++) { // external loop
+  for(let j = 0; j < n; j++) { // internal loop
+    string += "*";
+  }
+  // newline after each row
+  string += "\n";
+}
+// printing the string
+console.log(string);

From 6e5357c0fc299a7fa5b2b7c0ecf7d608624ab3e9 Mon Sep 17 00:00:00 2001
From: Ahmednae <91714237+Ahmednae@users.noreply.github.com>
Date: Sun, 31 Oct 2021 02:23:33 +0500
Subject: [PATCH 131/232] Create Right Triangle Pattern

---
 Javascript/Right Triangle Pattern | 15 +++++++++++++++
 1 file changed, 15 insertions(+)
 create mode 100644 Javascript/Right Triangle Pattern

diff --git a/Javascript/Right Triangle Pattern b/Javascript/Right Triangle Pattern
new file mode 100644
index 0000000..49a37ab
--- /dev/null
+++ b/Javascript/Right Triangle Pattern	
@@ -0,0 +1,15 @@
+To create a right triangle pattern in javascript, you'll need to work with three loops, one external and two internal. The external loop will execute internal loops 'n' times, and the internal loop will create a pattern for each row.
+let n = 5;
+let string = "";
+for (let i = 1; i <= n; i++) {
+  // printing spaces
+  for (let j = 0; j < n - i; j++) {
+    string += " ";
+  }
+  // printing star
+  for (let k = 0; k < i; k++) {
+    string += "*";
+  }
+  string += "\n";
+}
+console.log(string);

From de73bc69f4b4bc752f317a86fc7ebde91c85b07a Mon Sep 17 00:00:00 2001
From: Ahmednae <91714237+Ahmednae@users.noreply.github.com>
Date: Sun, 31 Oct 2021 02:27:52 +0500
Subject: [PATCH 132/232] Create Left triangle pattern

---
 Javascript/Left triangle pattern | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 Javascript/Left triangle pattern

diff --git a/Javascript/Left triangle pattern b/Javascript/Left triangle pattern
new file mode 100644
index 0000000..d131d75
--- /dev/null
+++ b/Javascript/Left triangle pattern	
@@ -0,0 +1,13 @@
+To create the left triangle pattern in javascript, run two nested for loops. The external loop will take care of the pattern's columns, while the internal loop will print the pattern's rows.
+
+You can see from the pattern above that we need to run an external loop for 'n' times while the internal loop runs once in the first execution, twice in the second execution, and so on until 'n' times.
+
+let n = 5;
+let string = "";
+for (let i = 1; i <= n; i++) {
+  for (let j = 0; j < i; j++) {
+    string += "*";
+  }
+  string += "\n";
+}
+console.log(string);

From 68abb073fa44c033f13d67a07e34faf9610c1564 Mon Sep 17 00:00:00 2001
From: Ahmednae <91714237+Ahmednae@users.noreply.github.com>
Date: Sun, 31 Oct 2021 02:31:14 +0500
Subject: [PATCH 133/232] Create hollow-triangle star

---
 Javascript/hollow-triangle star | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)
 create mode 100644 Javascript/hollow-triangle star

diff --git a/Javascript/hollow-triangle star b/Javascript/hollow-triangle star
new file mode 100644
index 0000000..43b6734
--- /dev/null
+++ b/Javascript/hollow-triangle star	
@@ -0,0 +1,28 @@
+To make a hollow triangle star pattern, follow these steps:
+
+Run two nested loops, the first for 'n' times and the second for one time for the first row, the second for two times for the second row, and so on until 'n' times.
+Print a star for the first and last positions in each row, as well as a space for other positions.
+In the final line, print a star in each position.
+Example
+
+let n = 6;
+let string = "";
+
+for (let i = 1; i <= n; i++) {
+  // printing star
+  for (let j = 0; j < i; j++) {
+    if(i === n) {
+      string += "*";
+    }
+    else {
+      if (j == 0 || j == i - 1) {
+        string += "*";
+      }
+      else {
+        string += " ";
+      }
+    }
+  }
+  string += "\n";
+}
+console.log(string);

From 413933e34b8cebf960ee2461bd4a66d4ec7159c0 Mon Sep 17 00:00:00 2001
From: Ahmednae <91714237+Ahmednae@users.noreply.github.com>
Date: Sun, 31 Oct 2021 02:38:55 +0500
Subject: [PATCH 134/232] Create hollow-triangle star

---
 Javascript/hollow-triangle star | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)
 create mode 100644 Javascript/hollow-triangle star

diff --git a/Javascript/hollow-triangle star b/Javascript/hollow-triangle star
new file mode 100644
index 0000000..43b6734
--- /dev/null
+++ b/Javascript/hollow-triangle star	
@@ -0,0 +1,28 @@
+To make a hollow triangle star pattern, follow these steps:
+
+Run two nested loops, the first for 'n' times and the second for one time for the first row, the second for two times for the second row, and so on until 'n' times.
+Print a star for the first and last positions in each row, as well as a space for other positions.
+In the final line, print a star in each position.
+Example
+
+let n = 6;
+let string = "";
+
+for (let i = 1; i <= n; i++) {
+  // printing star
+  for (let j = 0; j < i; j++) {
+    if(i === n) {
+      string += "*";
+    }
+    else {
+      if (j == 0 || j == i - 1) {
+        string += "*";
+      }
+      else {
+        string += " ";
+      }
+    }
+  }
+  string += "\n";
+}
+console.log(string);

From ef8f867681b1f7cd53ba15e85184100741870eb7 Mon Sep 17 00:00:00 2001
From: hannarajput <93453228+hannarajput@users.noreply.github.com>
Date: Sat, 30 Oct 2021 15:03:52 -0700
Subject: [PATCH 135/232] Create Simple-pattern

---
 C++/Simple-pattern | 35 +++++++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)
 create mode 100644 C++/Simple-pattern

diff --git a/C++/Simple-pattern b/C++/Simple-pattern
new file mode 100644
index 0000000..14ae39e
--- /dev/null
+++ b/C++/Simple-pattern
@@ -0,0 +1,35 @@
+// C++ code to demonstrate star pattern
+#include <iostream>
+using namespace std;
+
+// Function to demonstrate printing pattern
+void pypart(int n)
+{
+	// Outer loop to handle number of rows
+	// n in this case
+	int i = 0, j = 0;
+	while (i < n) {
+
+		// Inner loop to handle number of columns
+		// values changing acc. to outer loop
+		while (j <= i) {
+
+			// Printing stars
+			cout << "* ";
+			j++;
+		}
+		j = 0; // we have to reset j value so as it can
+			// start from beginning and print * equal to i.
+		i++;
+		// Ending line after each row
+		cout << endl;
+	}
+}
+
+// Driver Code
+int main()
+{
+	int n = 5;
+	pypart(n);
+	return 0;
+}

From 7e3fb00de10497fc55c9e080cc6ec709b5199d61 Mon Sep 17 00:00:00 2001
From: hannarajput <93453228+hannarajput@users.noreply.github.com>
Date: Sat, 30 Oct 2021 15:05:50 -0700
Subject: [PATCH 136/232] Create triangle pattern

---
 C++/triangle pattern | 37 +++++++++++++++++++++++++++++++++++++
 1 file changed, 37 insertions(+)
 create mode 100644 C++/triangle pattern

diff --git a/C++/triangle pattern b/C++/triangle pattern
new file mode 100644
index 0000000..fd3e268
--- /dev/null
+++ b/C++/triangle pattern	
@@ -0,0 +1,37 @@
+// C++ code to demonstrate star pattern
+#include <iostream>
+using namespace std;
+
+// Function to demonstrate printing pattern
+void pypart2(int n)
+{
+	int i = 0, j = 0, k = 0;
+	while (i < n) {
+	
+		// for spacing
+		while (k <= n - i - 2) {
+			cout << " ";
+			k++;
+		}
+		k = 0;
+	
+		// For Pattern printing
+		while (j < 2 * i - 1) {
+			cout << "*";
+			j++;
+		}
+		j = 0;
+		i++;
+		cout << endl;
+	}
+}
+
+// Driver Code
+int main()
+{
+	int n = 5;
+
+	// Function Call
+	pypart2(n);
+	return 0;
+}

From de07795d67cf8c85f370456cd4b42b6180a3f7e2 Mon Sep 17 00:00:00 2001
From: hannarajput <93453228+hannarajput@users.noreply.github.com>
Date: Sat, 30 Oct 2021 15:08:03 -0700
Subject: [PATCH 137/232] Create heart-shape-pattern

---
 C++/heart-shape-pattern | 68 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 68 insertions(+)
 create mode 100644 C++/heart-shape-pattern

diff --git a/C++/heart-shape-pattern b/C++/heart-shape-pattern
new file mode 100644
index 0000000..bde8df7
--- /dev/null
+++ b/C++/heart-shape-pattern
@@ -0,0 +1,68 @@
+The program demonstrate that how to make a heart shape using for loop in C++
+#include<iostream>
+using namespace std;
+int main()
+{
+ cout<<"\n\n\n\n\n";
+ 
+ for(int i=0;i<15;i++)
+ cout<<" ";
+ cout<<"*         *";
+ cout<<endl;
+ 
+ for(int j=0;j<13;j++)
+ cout<<" ";
+ cout<<"*   *     *   *";
+ cout<<endl;
+
+ for(int k=0;k<11;k++)
+ cout<<" ";
+ cout<<"*      *   *      *";
+ cout<<endl;
+
+ for(int l=0;l<11;l++)
+ cout<<" ";
+ cout<<"*       * *       *";
+ cout<<endl;
+ 
+ for(int m=0;m<11;m++)
+ cout<<" ";
+ cout<<"*        *        *";
+ cout<<endl;
+ 
+ for(int n=0;n<11;n++)
+ cout<<" ";
+ cout<<"*                 *";
+ cout<<endl;
+ 
+ for(int o=0;o<12;o++)
+ cout<<" ";
+ cout<<"*               *";
+ cout<<endl; 
+
+ for(int p=0;p<13;p++)
+ cout<<" ";
+ cout<<"*             *";
+ cout<<endl;
+ 
+ for(int q=0;q<14;q++)
+ cout<<" ";
+ cout<<"*           *";
+ cout<<endl;
+ 
+ for(int r=0;r<15;r++)
+ cout<<" ";
+ cout<<"*         *";
+ cout<<endl;
+ 
+ for(int s=0;s<17;s++)
+ cout<<" ";
+ cout<<"*     *";
+ cout<<endl;
+ 
+ for(int t=0;t<20;t++)
+ cout<<" ";
+ cout<<"**";
+ cout<<endl;
+ return 0;
+}

From ea283958fe62ea260debeda470bbf0b4da43ff84 Mon Sep 17 00:00:00 2001
From: hannarajput <93453228+hannarajput@users.noreply.github.com>
Date: Sat, 30 Oct 2021 15:12:49 -0700
Subject: [PATCH 138/232] Create dice sum program

---
 C++/dice sum program | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)
 create mode 100644 C++/dice sum program

diff --git a/C++/dice sum program b/C++/dice sum program
new file mode 100644
index 0000000..3bb6723
--- /dev/null
+++ b/C++/dice sum program	
@@ -0,0 +1,27 @@
+Consider the following three C++ programmes. The first programme is fairly basic, consisting of a function called 'dice' that adds the face values of two dice. The 'dice' function was then called into the main function. The result is saved in an array in the second programme. We then used that array in the third programme to count each possible total of the two dices (2 to 12).
+
+In C++, create a function 'dice' that simulates the throwing of two dice and returns the sum of their face values as an int.
+
+Create a C++ function called 'getResult' that saves the result of the function 'dice' as an array.
+
+#include <iostream>
+#include <cstdlib>
+#include <ctime>
+using namespace std;
+int dice();
+int main()
+{
+srand(time(0));
+for (int i =0; i<10; i++)
+{
+int number = dice();
+cout << number << endl;
+}
+}
+int dice()
+{
+int dice1 = (int)(1+(rand()%6));
+int dice2 = (int)(1+(rand()%6));
+int diceSum = dice1 + dice2;
+return diceSum;
+}

From f6ac5a47c5aac164f3b9faa6270737a73b851764 Mon Sep 17 00:00:00 2001
From: QuratAin <quratulainshoro1@outlook.com>
Date: Sun, 31 Oct 2021 11:31:32 +0500
Subject: [PATCH 139/232] Added new Js code

---
 Javascript/Animations.js | 18 ++++++++++++++++++
 Javascript/index.html    | 31 +++++++++++++++++++++++++++++++
 2 files changed, 49 insertions(+)
 create mode 100644 Javascript/Animations.js
 create mode 100644 Javascript/index.html

diff --git a/Javascript/Animations.js b/Javascript/Animations.js
new file mode 100644
index 0000000..d53ba9e
--- /dev/null
+++ b/Javascript/Animations.js
@@ -0,0 +1,18 @@
+function myAnimation(){
+    let id = null;
+    const box = document.getElementById("animationBox");
+    let pos = 0;
+    clearInterval(id);
+    id = setInterval(frame, 5);
+    
+    function frame(){
+        if(pos == 350){
+            clearInterval(id);
+        }
+        else{
+            pos++;
+            box.style.top = pos + "px";
+            box.style.left = pos + "px";
+        }
+    }
+}
\ No newline at end of file
diff --git a/Javascript/index.html b/Javascript/index.html
new file mode 100644
index 0000000..6a1dcc3
--- /dev/null
+++ b/Javascript/index.html
@@ -0,0 +1,31 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <script src="Animations.js"></script>
+    <title>Javascript HTML DOM Animations</title>
+    <style>
+        #Container{
+            width: 400px;
+            height: 400px;
+            position: relative;
+            background: orangered;
+        }
+        #animationBox{
+            width: 50px;
+            height: 50px;
+            position: absolute;
+            background-color: yellow;
+        }
+    </style>
+</head>
+<body>
+    <p><button onclick="myAnimation()">Click to Animate</button></p>
+    <div id="Container">
+        <div id="animationBox">
+        </div>
+    </div>
+</body>
+</html>
\ No newline at end of file

From 76fb890cfdad14d736e662895cdec107c0a93aa7 Mon Sep 17 00:00:00 2001
From: Qurat Ul Ain Shoro <65081454+QuratAin@users.noreply.github.com>
Date: Sun, 31 Oct 2021 11:52:16 +0500
Subject: [PATCH 140/232] Add files via upload

---
 Javascript/Animations.js | 18 ++++++++++++++++++
 Javascript/index.html    | 31 +++++++++++++++++++++++++++++++
 2 files changed, 49 insertions(+)
 create mode 100644 Javascript/Animations.js
 create mode 100644 Javascript/index.html

diff --git a/Javascript/Animations.js b/Javascript/Animations.js
new file mode 100644
index 0000000..1c20952
--- /dev/null
+++ b/Javascript/Animations.js
@@ -0,0 +1,18 @@
+function myAnimation(){
+    let id = null;
+    const box = document.getElementById("animationBox");
+    let pos = 0;
+    clearInterval(id);
+    id = setInterval(frame, 5);
+    
+    function frame(){
+        if(pos == 350){
+            clearInterval(id);
+        }
+        else{
+            pos++;
+            box.style.top = pos + "px";
+            box.style.left = pos + "px";
+        }
+    }
+}
\ No newline at end of file
diff --git a/Javascript/index.html b/Javascript/index.html
new file mode 100644
index 0000000..3349d53
--- /dev/null
+++ b/Javascript/index.html
@@ -0,0 +1,31 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta http-equiv="X-UA-Compatible" content="IE=edge">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <script src="Animations.js"></script>
+    <title>Javascript HTML DOM Animations</title>
+    <style>
+        #Container{
+            width: 400px;
+            height: 400px;
+            position: relative;
+            background: orangered;
+        }
+        #animationBox{
+            width: 50px;
+            height: 50px;
+            position: absolute;
+            background-color: yellow;
+        }
+    </style>
+</head>
+<body>
+    <p><button onclick="myAnimation()">Click to Animate</button></p>
+    <div id="Container">
+        <div id="animationBox">
+        </div>
+    </div>
+</body>
+</html>
\ No newline at end of file

From 0299ad49d5750b0b787fc2b86a60bef6ca0d1368 Mon Sep 17 00:00:00 2001
From: QuratAin <quratulainshoro1@outlook.com>
Date: Sun, 31 Oct 2021 12:16:49 +0500
Subject: [PATCH 141/232] JS graphics using Charts.js

---
 Javascript/JSCharts.html | 55 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 55 insertions(+)
 create mode 100644 Javascript/JSCharts.html

diff --git a/Javascript/JSCharts.html b/Javascript/JSCharts.html
new file mode 100644
index 0000000..bcfeadc
--- /dev/null
+++ b/Javascript/JSCharts.html
@@ -0,0 +1,55 @@
+<!DOCTYPE html>
+<html>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/2.9.4/Chart.js"></script>
+<script src="Charts.js"></script>
+<body>
+<h2>Chart.js comes with the following built-in chart types:</h2>
+<ul>
+<li>Scatter</li>
+<li>Line</li>
+<li>Bar</li>
+<li>Radar</li>
+<li>Pie and Doughnut</li>
+<li>Polar Area</li>
+<li>Bubble</li>
+</ul>
+<h3>Following is the example of a Scatter Plot</h3><br>
+<canvas id="myChart" style="width:100%;max-width:700px"></canvas>
+
+<script>
+var xyValues = [
+  {x:50, y:7},
+  {x:60, y:8},
+  {x:70, y:8},
+  {x:80, y:9},
+  {x:90, y:9},
+  {x:100, y:9},
+  {x:110, y:10},
+  {x:120, y:11},
+  {x:130, y:14},
+  {x:140, y:14},
+  {x:150, y:15}
+];
+
+new Chart("myChart", {
+  type: "scatter",
+  data: {
+    datasets: [{
+      pointRadius: 4,
+      pointBackgroundColor: "rgb(0,0,255)",
+      data: xyValues
+    }]
+  },
+  options: {
+    legend: {display: false},
+    scales: {
+      xAxes: [{ticks: {min: 40, max:160}}],
+      yAxes: [{ticks: {min: 6, max:16}}],
+    }
+  }
+});
+</script>
+</body>
+</html>
+
+

From ab78b7b0664f70f7f4123c2976e35f3a53c540c2 Mon Sep 17 00:00:00 2001
From: Aqsa Umar <aksaumar35@gmail.com>
Date: Sun, 31 Oct 2021 12:18:26 +0500
Subject: [PATCH 142/232] GGPLOTS

---
 R-Programming/GGPLOTS | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)
 create mode 100644 R-Programming/GGPLOTS

diff --git a/R-Programming/GGPLOTS b/R-Programming/GGPLOTS
new file mode 100644
index 0000000..5fd2cc8
--- /dev/null
+++ b/R-Programming/GGPLOTS
@@ -0,0 +1,21 @@
+install.packages("ggplot2")
+library(ggplot2)
+help("ggplot")
+x <- iris
+#ggplot(data=x,aes(x=x$Species, y=x$sepal.length, fill=x$Species))+
+ # geom_boxplot()
+
+
+ggplot(data = x, aes(x=x$Species, y=x$Sepal.Length, fill=x$Species))+ 
+  geom_boxplot()+
+  labs(title = "Bar plot of iris", x="Species", y="Sepal length(cm)"
+       ,fill="Species")
+
+#barplot
+ggplot(data = x, aes(x=x$Species, y=x$Sepal.Length, fill=x$Species))+ 
+  geom_bar(position="dodge", stat="identity")+
+  facet_wrap(~x$Species)+
+   labs(title = "Bar plot of iris", x="Species", y="Sepal length(cm)"
+   ,fill="Species")
+help("ggplot")
+  

From 7570cb1b8d75eee6650abb93f86479dd75fe4cc5 Mon Sep 17 00:00:00 2001
From: Qurat Ul Ain Shoro <65081454+QuratAin@users.noreply.github.com>
Date: Sun, 31 Oct 2021 12:23:26 +0500
Subject: [PATCH 143/232] JS graphs using Chart.js

---
 Javascript/JSCharts.html | 55 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 55 insertions(+)
 create mode 100644 Javascript/JSCharts.html

diff --git a/Javascript/JSCharts.html b/Javascript/JSCharts.html
new file mode 100644
index 0000000..d4e4786
--- /dev/null
+++ b/Javascript/JSCharts.html
@@ -0,0 +1,55 @@
+<!DOCTYPE html>
+<html>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/2.9.4/Chart.js"></script>
+<script src="Charts.js"></script>
+<body>
+<h2>Chart.js comes with the following built-in chart types:</h2>
+<ul>
+<li>Scatter</li>
+<li>Line</li>
+<li>Bar</li>
+<li>Radar</li>
+<li>Pie and Doughnut</li>
+<li>Polar Area</li>
+<li>Bubble</li>
+</ul>
+<h3>Following is the example of a Scatter Plot</h3><br>
+<canvas id="myChart" style="width:100%;max-width:700px"></canvas>
+
+<script>
+var xyValues = [
+  {x:50, y:7},
+  {x:60, y:8},
+  {x:70, y:8},
+  {x:80, y:9},
+  {x:90, y:9},
+  {x:100, y:9},
+  {x:110, y:10},
+  {x:120, y:11},
+  {x:130, y:14},
+  {x:140, y:14},
+  {x:150, y:15}
+];
+
+new Chart("myChart", {
+  type: "scatter",
+  data: {
+    datasets: [{
+      pointRadius: 4,
+      pointBackgroundColor: "rgb(0,0,255)",
+      data: xyValues
+    }]
+  },
+  options: {
+    legend: {display: false},
+    scales: {
+      xAxes: [{ticks: {min: 40, max:160}}],
+      yAxes: [{ticks: {min: 6, max:16}}],
+    }
+  }
+});
+</script>
+</body>
+</html>
+
+

From ce186d74888a256db19d3fd4c29b78a20693c97e Mon Sep 17 00:00:00 2001
From: Qurat Ul Ain Shoro <65081454+QuratAin@users.noreply.github.com>
Date: Sun, 31 Oct 2021 12:24:51 +0500
Subject: [PATCH 144/232] Delete JSCharts.html

---
 Javascript/JSCharts.html | 55 ----------------------------------------
 1 file changed, 55 deletions(-)
 delete mode 100644 Javascript/JSCharts.html

diff --git a/Javascript/JSCharts.html b/Javascript/JSCharts.html
deleted file mode 100644
index d4e4786..0000000
--- a/Javascript/JSCharts.html
+++ /dev/null
@@ -1,55 +0,0 @@
-<!DOCTYPE html>
-<html>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/2.9.4/Chart.js"></script>
-<script src="Charts.js"></script>
-<body>
-<h2>Chart.js comes with the following built-in chart types:</h2>
-<ul>
-<li>Scatter</li>
-<li>Line</li>
-<li>Bar</li>
-<li>Radar</li>
-<li>Pie and Doughnut</li>
-<li>Polar Area</li>
-<li>Bubble</li>
-</ul>
-<h3>Following is the example of a Scatter Plot</h3><br>
-<canvas id="myChart" style="width:100%;max-width:700px"></canvas>
-
-<script>
-var xyValues = [
-  {x:50, y:7},
-  {x:60, y:8},
-  {x:70, y:8},
-  {x:80, y:9},
-  {x:90, y:9},
-  {x:100, y:9},
-  {x:110, y:10},
-  {x:120, y:11},
-  {x:130, y:14},
-  {x:140, y:14},
-  {x:150, y:15}
-];
-
-new Chart("myChart", {
-  type: "scatter",
-  data: {
-    datasets: [{
-      pointRadius: 4,
-      pointBackgroundColor: "rgb(0,0,255)",
-      data: xyValues
-    }]
-  },
-  options: {
-    legend: {display: false},
-    scales: {
-      xAxes: [{ticks: {min: 40, max:160}}],
-      yAxes: [{ticks: {min: 6, max:16}}],
-    }
-  }
-});
-</script>
-</body>
-</html>
-
-

From 7c5bf67f7c78e771cf649fd6c021fd9782f58016 Mon Sep 17 00:00:00 2001
From: QuratAin <quratulainshoro1@outlook.com>
Date: Sun, 31 Oct 2021 12:47:29 +0500
Subject: [PATCH 145/232] JS calculator

---
 Javascript/JsCalculator.html | 71 ++++++++++++++++++++++++++++++++++++
 1 file changed, 71 insertions(+)
 create mode 100644 Javascript/JsCalculator.html

diff --git a/Javascript/JsCalculator.html b/Javascript/JsCalculator.html
new file mode 100644
index 0000000..abbd33c
--- /dev/null
+++ b/Javascript/JsCalculator.html
@@ -0,0 +1,71 @@
+<!DOCTYPE html>
+<html>
+<body>
+<div class = title ><h2>JS Calculator</h2></div>
+<table border="2" cellpadding="10">
+<tr>
+<td><input type="button" value="c" onclick="clr()"/> </td>
+<td colspan="3"><input type="text" id="textval"/></td>
+</tr>
+<tr>
+<td><input type="button" value="+" onclick="display('+')"/> </td>
+<td><input type="button" value="1" onclick="display('1')"/> </td>
+<td><input type="button" value="2" onclick="display('2')"/> </td>
+<td><input type="button" value="3" onclick="display('3')"/> </td>
+</tr>
+<tr>
+<td><input type="button" value="-" onclick="display('-')"/> </td>
+<td><input type="button" value="4" onclick="display('4')"/> </td>
+<td><input type="button" value="5" onclick="display('5')"/> </td>
+<td><input type="button" value="6" onclick="display('6')"/> </td>
+</tr>
+<tr>
+<td><input type="button" value="*" onclick="display('*')"/> </td>
+<td><input type="button" value="7" onclick="display('7')"/> </td>
+<td><input type="button" value="8" onclick="display('8')"/> </td>
+<td><input type="button" value="9" onclick="display('9')"/> </td>
+</tr>
+<tr>
+<td><input type="button" value="/" onclick="display('/')"/> </td>
+<td><input type="button" value="." onclick="display('.')"/> </td>
+<td><input type="button" value="0" onclick="display('0')"/> </td>
+<td><input type="button" value="=" onclick="evaluate()"/> </td>
+</tr>
+</table>
+</body>
+<script>
+function display(val)
+{
+ document.getElementById("textval").value+=val
+ }
+function evaluate()
+{
+ let x = document.getElementById("textval").value
+ let y = eval(x)
+ document.getElementById("textval").value = y
+}
+function clr()
+{
+ document.getElementById("textval").value = ""
+}
+</script>
+<style>
+input[type="button"]
+{
+border-radius: 10px;
+background-color:orchid;
+color: white;
+border-color:black ;
+width:100%;
+}
+input[type="text"]
+{
+border-radius: 10px;
+text-align: right;
+background-color:black;
+color: white;
+border-color: white;
+width:100%
+}
+</style>
+</html>

From b87e6665d167e831a72ed8e150a2a3767ac3ac10 Mon Sep 17 00:00:00 2001
From: Qurat Ul Ain Shoro <65081454+QuratAin@users.noreply.github.com>
Date: Sun, 31 Oct 2021 13:00:49 +0500
Subject: [PATCH 146/232] JS calculator using global JS functions

---
 Javascript/JsCalculator.html | 71 ++++++++++++++++++++++++++++++++++++
 1 file changed, 71 insertions(+)
 create mode 100644 Javascript/JsCalculator.html

diff --git a/Javascript/JsCalculator.html b/Javascript/JsCalculator.html
new file mode 100644
index 0000000..1298e03
--- /dev/null
+++ b/Javascript/JsCalculator.html
@@ -0,0 +1,71 @@
+<!DOCTYPE html>
+<html>
+<body>
+<div class = title ><h2>JS Calculator</h2></div>
+<table border="2" cellpadding="10">
+<tr>
+<td><input type="button" value="c" onclick="clr()"/> </td>
+<td colspan="3"><input type="text" id="textval"/></td>
+</tr>
+<tr>
+<td><input type="button" value="+" onclick="display('+')"/> </td>
+<td><input type="button" value="1" onclick="display('1')"/> </td>
+<td><input type="button" value="2" onclick="display('2')"/> </td>
+<td><input type="button" value="3" onclick="display('3')"/> </td>
+</tr>
+<tr>
+<td><input type="button" value="-" onclick="display('-')"/> </td>
+<td><input type="button" value="4" onclick="display('4')"/> </td>
+<td><input type="button" value="5" onclick="display('5')"/> </td>
+<td><input type="button" value="6" onclick="display('6')"/> </td>
+</tr>
+<tr>
+<td><input type="button" value="*" onclick="display('*')"/> </td>
+<td><input type="button" value="7" onclick="display('7')"/> </td>
+<td><input type="button" value="8" onclick="display('8')"/> </td>
+<td><input type="button" value="9" onclick="display('9')"/> </td>
+</tr>
+<tr>
+<td><input type="button" value="/" onclick="display('/')"/> </td>
+<td><input type="button" value="." onclick="display('.')"/> </td>
+<td><input type="button" value="0" onclick="display('0')"/> </td>
+<td><input type="button" value="=" onclick="evaluate()"/> </td>
+</tr>
+</table>
+</body>
+<script>
+function display(val)
+{
+ document.getElementById("textval").value+=val
+ }
+function evaluate()
+{
+ let x = document.getElementById("textval").value
+ let y = eval(x)
+ document.getElementById("textval").value = y
+}
+function clr()
+{
+ document.getElementById("textval").value = ""
+}
+</script>
+<style>
+input[type="button"]
+{
+border-radius: 10px;
+background-color:orchid;
+color: white;
+border-color:black ;
+width:100%;
+}
+input[type="text"]
+{
+border-radius: 10px;
+text-align: right;
+background-color:black;
+color: white;
+border-color: white;
+width:100%
+}
+</style>
+</html>

From 8e4891f63bed4554c1ea8afec171decc574bb8a0 Mon Sep 17 00:00:00 2001
From: codewithwaqar <88764001+codewithwaqar@users.noreply.github.com>
Date: Sun, 31 Oct 2021 13:19:15 +0500
Subject: [PATCH 147/232] CallableClass

Basic Example of CallableClass
---
 Dart/CallableClass.dart | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)
 create mode 100644 Dart/CallableClass.dart

diff --git a/Dart/CallableClass.dart b/Dart/CallableClass.dart
new file mode 100644
index 0000000..b70e91c
--- /dev/null
+++ b/Dart/CallableClass.dart
@@ -0,0 +1,19 @@
+/* 
+ * Dart allows the user to create a callable class which allows the 
+ * instance of the class to be called as a function. To allow an 
+ * instance of your Dart class to be called like a function, implement 
+ * the call() method. 
+ */
+void main() {
+  Student std1 = Student();
+  int age = std1('Waqar Ali Siyal', 21);
+  print(age);
+}
+
+class Student {
+  int call(String name, int age) {
+//     print('This is call method');
+//     print('$name $age');
+    return age;
+  }
+}

From baa47c06aae4977d699dbc0f4df10965c73e8368 Mon Sep 17 00:00:00 2001
From: Qurat Ul Ain Shoro <65081454+QuratAin@users.noreply.github.com>
Date: Sun, 31 Oct 2021 13:20:48 +0500
Subject: [PATCH 148/232] Delete Animations.js

---
 Javascript/Animations.js | 18 ------------------
 1 file changed, 18 deletions(-)
 delete mode 100644 Javascript/Animations.js

diff --git a/Javascript/Animations.js b/Javascript/Animations.js
deleted file mode 100644
index 1c20952..0000000
--- a/Javascript/Animations.js
+++ /dev/null
@@ -1,18 +0,0 @@
-function myAnimation(){
-    let id = null;
-    const box = document.getElementById("animationBox");
-    let pos = 0;
-    clearInterval(id);
-    id = setInterval(frame, 5);
-    
-    function frame(){
-        if(pos == 350){
-            clearInterval(id);
-        }
-        else{
-            pos++;
-            box.style.top = pos + "px";
-            box.style.left = pos + "px";
-        }
-    }
-}
\ No newline at end of file

From 9b065e3bed536e105bd41ad52b982c52b98f4dd4 Mon Sep 17 00:00:00 2001
From: Qurat Ul Ain Shoro <65081454+QuratAin@users.noreply.github.com>
Date: Sun, 31 Oct 2021 13:20:59 +0500
Subject: [PATCH 149/232] Delete index.html

---
 Javascript/index.html | 31 -------------------------------
 1 file changed, 31 deletions(-)
 delete mode 100644 Javascript/index.html

diff --git a/Javascript/index.html b/Javascript/index.html
deleted file mode 100644
index 3349d53..0000000
--- a/Javascript/index.html
+++ /dev/null
@@ -1,31 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta http-equiv="X-UA-Compatible" content="IE=edge">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <script src="Animations.js"></script>
-    <title>Javascript HTML DOM Animations</title>
-    <style>
-        #Container{
-            width: 400px;
-            height: 400px;
-            position: relative;
-            background: orangered;
-        }
-        #animationBox{
-            width: 50px;
-            height: 50px;
-            position: absolute;
-            background-color: yellow;
-        }
-    </style>
-</head>
-<body>
-    <p><button onclick="myAnimation()">Click to Animate</button></p>
-    <div id="Container">
-        <div id="animationBox">
-        </div>
-    </div>
-</body>
-</html>
\ No newline at end of file

From f58e00e83a1b39b119a186e13ff6f8465ae01447 Mon Sep 17 00:00:00 2001
From: codewithwaqar <88764001+codewithwaqar@users.noreply.github.com>
Date: Sun, 31 Oct 2021 13:23:12 +0500
Subject: [PATCH 150/232] Closures

Basic Example of Closures in Dart
---
 Dart/ClosuresInDart.dart | 26 ++++++++++++++++++++++++++
 1 file changed, 26 insertions(+)
 create mode 100644 Dart/ClosuresInDart.dart

diff --git a/Dart/ClosuresInDart.dart b/Dart/ClosuresInDart.dart
new file mode 100644
index 0000000..8acd6d4
--- /dev/null
+++ b/Dart/ClosuresInDart.dart
@@ -0,0 +1,26 @@
+// The values of parent scope variables can be modified within a closure
+
+void main() {
+  // A closure is a function that has access to the parent scope, even after the
+  // scope has closed
+  String msg = 'Hello';
+  Function greet = () {
+    msg = 'Good Night';
+    print(msg);
+  };
+  greet();
+
+  // A closure is a function object that has access to variables in its lexical scope, even
+  // when the function is used outside of its original scope
+  Function outsideFunc = () {
+    String msg = 'Outside Func';
+    Function insideFunc = () {
+      msg = 'Inside Func';
+      print(msg);
+    };
+    return insideFunc;
+  };
+
+  Function funcObj = outsideFunc(); // inside Func
+  funcObj(); // Calling insideFunc
+}

From 5e7f88872e7675af2122650fa2a8ee445bbacd3b Mon Sep 17 00:00:00 2001
From: codewithwaqar <88764001+codewithwaqar@users.noreply.github.com>
Date: Sun, 31 Oct 2021 13:26:41 +0500
Subject: [PATCH 151/232] Fixed Length List

Example of Fixed Length List
---
 Dart/FixedLengthList.dart | 41 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 41 insertions(+)
 create mode 100644 Dart/FixedLengthList.dart

diff --git a/Dart/FixedLengthList.dart b/Dart/FixedLengthList.dart
new file mode 100644
index 0000000..32d4f69
--- /dev/null
+++ b/Dart/FixedLengthList.dart
@@ -0,0 +1,41 @@
+/* 
+ * List is a collection of items. A list is referenced by a variable name. The items in the 
+ * list can be accessed using index. Index starts with 0.
+ * Types of List
+ * 1. Fixed Length List
+ * 2. Growable List
+ * In this video we will cover Fixed Length List
+*/ 
+void main() {
+  // Understand List
+  // 5 elements, index from 0 - 4
+  // 8 elements, index from 0 - 7
+  // 0 - First Element
+  // 1 - Second Element
+  // and so on upto (length of list - 1)
+//   var oddNums = List(5);
+//   print(oddNums[0]);
+  var oddNums = List<int>.filled(5, 0);
+//   oddNums.length = 10;  // error
+//   oddNums.add(1);       // error
+  oddNums[0] = 1;
+  oddNums[1] = 3;
+  oddNums[2] = 5;
+  oddNums[3] = 7;
+  oddNums[4] = 9;
+  
+//   print(oddNums[3]);
+  
+  // Print all elements using for loop
+//   for(int i=0; i<oddNums.length; i++) {
+//     print(oddNums[i]);
+//   }
+  
+  // Print all elements using for in loop
+//   for(int element in oddNums) {
+//     print(element);
+//   }
+  
+  // Print all elements using for each loop
+  oddNums.forEach((element) => print(element));
+}

From 1ce4364a1998aa88d85e3e4cab1288076dd40770 Mon Sep 17 00:00:00 2001
From: codewithwaqar <88764001+codewithwaqar@users.noreply.github.com>
Date: Sun, 31 Oct 2021 13:29:27 +0500
Subject: [PATCH 152/232] Growable List

Example of Growable List
---
 Dart/GrowableList.dart | 63 ++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 63 insertions(+)
 create mode 100644 Dart/GrowableList.dart

diff --git a/Dart/GrowableList.dart b/Dart/GrowableList.dart
new file mode 100644
index 0000000..5fa3306
--- /dev/null
+++ b/Dart/GrowableList.dart
@@ -0,0 +1,63 @@
+/* 
+ * List is a collection of items. A list is referenced by a variable name. The items in the 
+ * list can be accessed using index. Index starts with 0.
+ * Types of List
+ * 1. Fixed Length List
+ * 2. Growable List
+ * In this video we will cover Growable List
+*/ 
+void main() {
+  // Understand List
+  // 5 elements, index from 0 - 4
+  // 8 elements, index from 0 - 7
+  // 0 - First Element
+  // 1 - Second Element
+  // and so on upto (length of list - 1)
+  List<int> oddNums = [];
+  oddNums.add(1);
+  oddNums.add(3);
+  oddNums.add(5);
+  oddNums.add(7);
+  oddNums.add(9);
+//   print(oddNums.length);
+//   print(oddNums);
+  
+//   oddNums.remove(3);
+//   print(oddNums.length);
+//   print(oddNums);
+  
+//   oddNums.removeAt(2);
+//   print(oddNums.length);
+//   print(oddNums);
+  
+//   oddNums.add(11);
+//   print(oddNums);
+  
+//   oddNums.insert(1, 3);
+//   oddNums.insert(3, 7);
+//   print(oddNums);
+  
+//   oddNums.clear();
+//   print(oddNums);
+  
+//   print(oddNums[3]);
+  
+  // Print all elements using for loop
+//   for(int i=0; i<oddNums.length; i++) {
+//     print(oddNums[i]);
+//   }
+  
+  // Print all elements using for in loop
+//   for(int element in oddNums) {
+//     print(element);
+//   }
+  
+  // Print all elements using for each loop
+//   oddNums.forEach((element) => print(element));
+  
+  var weekDays = ['Monday', 'Tuesday', 'Wednesday'];
+  weekDays.add('Thursday');
+  weekDays.add('Friday');
+  print(weekDays.length);
+  print(weekDays);
+}

From 78c4fa6720ed372caeaa45fe6da387af0f9bf654 Mon Sep 17 00:00:00 2001
From: codewithwaqar <88764001+codewithwaqar@users.noreply.github.com>
Date: Sun, 31 Oct 2021 13:35:14 +0500
Subject: [PATCH 153/232] Map

Example of Map in Dart
---
 Dart/Map.dart | 53 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 53 insertions(+)
 create mode 100644 Dart/Map.dart

diff --git a/Dart/Map.dart b/Dart/Map.dart
new file mode 100644
index 0000000..0ab5788
--- /dev/null
+++ b/Dart/Map.dart
@@ -0,0 +1,53 @@
+/*
+ * The Map object is a simple key/value pair. Keys and values in a map may be of 
+ * any type. A Map is a dynamic collection. In other words, Maps can grow and shrink 
+ * at runtime.
+ * Maps can be declared in two ways:
+ * 1. Using a Map constructor
+ * 2. Using Map Literals
+ * 
+ * Note:
+ * -> Key has to be unique
+ * -> Value can be duplicate
+*/
+void main() {
+  // Using map constructor
+  var squares = Map();
+  squares['1'] = 1;
+  squares['2'] = 4;
+  squares['3'] = 9;
+  squares['4'] = 16;
+  squares['5'] = 25;
+  
+//   print(squares);
+  
+  // print all keys
+//   for(var key in squares.keys) {
+//     print(key);
+//   }
+  
+  // print all values
+//   for(var value in squares.values) {
+//     print(value);
+//   }
+  
+  // print all key value pairs
+//   squares.forEach((key, value) => print('Key: $key, value: $value'));
+  
+  print(squares.containsKey('3'));
+  print(squares.isEmpty);
+  print(squares.length);
+  
+  squares.remove('4');
+  print(squares);
+  
+  // Using map literal
+  var userMap = {
+    'name' : 'Waqar Ali Siyal',
+    'age' : 21,
+    'rollNo' : '18SW82',
+  };
+  
+//   print(userMap);
+//   print(userMap['name']);
+}

From eb03266cdd7b56514af06f5f915f6627bd9adcfe Mon Sep 17 00:00:00 2001
From: codewithwaqar <88764001+codewithwaqar@users.noreply.github.com>
Date: Sun, 31 Oct 2021 13:36:13 +0500
Subject: [PATCH 154/232] Set

Example of Set in Dart
---
 Dart/Set.dart | 50 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 50 insertions(+)
 create mode 100644 Dart/Set.dart

diff --git a/Dart/Set.dart b/Dart/Set.dart
new file mode 100644
index 0000000..57200dc
--- /dev/null
+++ b/Dart/Set.dart
@@ -0,0 +1,50 @@
+/* 
+ * In Dart, Set is a unordered list of distinct values of same types. Set is 
+ * much similar to an array but it is unordered and won’t allows duplicate elements, 
+ * each value contained in a set is unique. You cannot get elements by index, since the
+ * items are unordered.
+ * There are two ways we can declare/initialize an empty set, use {} preceded by a 
+ * type argument, or assign {} to a variable of type Set.
+ * 1. var setName = <type>{};
+ * 2. Set<type> setName = {};
+ * Note: The syntax for map literals is similar to that for set literals. If you 
+ * forget the type annotation with {} or with the variable it’s assigned to, 
+ * then Dart would creates an Map object instead of Set.
+*/ 
+void main() {
+  var oddNums = <int>{};
+  oddNums.add(1);
+  oddNums.add(3);
+  oddNums.add(5);
+  oddNums.add(7);
+  
+  oddNums.add(3);       // Duplicate entries are ignored
+  
+  print(oddNums);
+  
+  Set<int> evenNums = {2, 4, 6, 8, 4, 10};
+  evenNums.add(12);
+  print(evenNums);
+  
+  print(evenNums.contains(6));
+  print(oddNums.contains(2));
+  
+  evenNums.remove(12);
+  print(evenNums);
+  
+  print(evenNums.isEmpty);
+  evenNums.clear();
+  print(evenNums);
+  print(oddNums.length);
+
+  oddNums.addAll({9,11,13});
+  print(oddNums);
+
+  // Set from List
+  var weekDays = ['Monday', 'Tuesday', 'Wednesday'];
+  var weekDaysSet = Set.from(weekDays);
+  print(weekDaysSet);
+  weekDaysSet.add('Friday');
+  weekDaysSet.add('Thursday');
+  print(weekDaysSet);
+}

From 7bbc581674c11ea005c564d667a96392f95c1581 Mon Sep 17 00:00:00 2001
From: QuratAin <quratulainshoro1@outlook.com>
Date: Sun, 31 Oct 2021 13:44:15 +0500
Subject: [PATCH 155/232] Added code to create graphs using chart.js

---
 Javascript/JSCharts.html | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Javascript/JSCharts.html b/Javascript/JSCharts.html
index bcfeadc..1e9cd04 100644
--- a/Javascript/JSCharts.html
+++ b/Javascript/JSCharts.html
@@ -2,6 +2,7 @@
 <html>
 <script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/2.9.4/Chart.js"></script>
 <script src="Charts.js"></script>
+<title>Chart.js</title>
 <body>
 <h2>Chart.js comes with the following built-in chart types:</h2>
 <ul>

From 2dc8762f64b54552a2ef6421020ee1855dd45e94 Mon Sep 17 00:00:00 2001
From: QuratAin <quratulainshoro1@outlook.com>
Date: Sun, 31 Oct 2021 14:15:30 +0500
Subject: [PATCH 156/232] Added code to create graphs using chart.js

---
 Javascript/JSCharts.html | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

diff --git a/Javascript/JSCharts.html b/Javascript/JSCharts.html
index 1e9cd04..787d6cc 100644
--- a/Javascript/JSCharts.html
+++ b/Javascript/JSCharts.html
@@ -4,16 +4,7 @@
 <script src="Charts.js"></script>
 <title>Chart.js</title>
 <body>
-<h2>Chart.js comes with the following built-in chart types:</h2>
-<ul>
-<li>Scatter</li>
-<li>Line</li>
-<li>Bar</li>
-<li>Radar</li>
-<li>Pie and Doughnut</li>
-<li>Polar Area</li>
-<li>Bubble</li>
-</ul>
+<h2>Chart.js comes with built-in chart types.</h2>
 <h3>Following is the example of a Scatter Plot</h3><br>
 <canvas id="myChart" style="width:100%;max-width:700px"></canvas>
 

From 250d1a23affdda9f049874f0718864b42a013c64 Mon Sep 17 00:00:00 2001
From: msidrisi91 <mohdshaheem91@gmail.com>
Date: Sun, 31 Oct 2021 14:47:28 +0530
Subject: [PATCH 157/232] Added Python Programs

---
 Python/firstandlastelementinsortedarray.py | 50 +++++++++++++++
 Python/foursum.py                          | 53 ++++++++++++++++
 Python/generateparanthesis.py              | 29 +++++++++
 Python/lowestcommonancestor.py             | 55 +++++++++++++++++
 Python/mergeKLL.py                         | 71 ++++++++++++++++++++++
 Python/mergeLL.py                          | 43 +++++++++++++
 Python/partitiondisjoint.py                | 45 ++++++++++++++
 Python/searchhinsortedarray.py             | 10 +++
 Python/sudoku_solver.py                    | 63 +++++++++++++++++++
 Python/sumequalszero.py                    | 55 +++++++++++++++++
 Python/threesumclosest.py                  | 34 +++++++++++
 11 files changed, 508 insertions(+)
 create mode 100644 Python/firstandlastelementinsortedarray.py
 create mode 100644 Python/foursum.py
 create mode 100644 Python/generateparanthesis.py
 create mode 100644 Python/lowestcommonancestor.py
 create mode 100644 Python/mergeKLL.py
 create mode 100644 Python/mergeLL.py
 create mode 100644 Python/partitiondisjoint.py
 create mode 100644 Python/searchhinsortedarray.py
 create mode 100644 Python/sudoku_solver.py
 create mode 100644 Python/sumequalszero.py
 create mode 100644 Python/threesumclosest.py

diff --git a/Python/firstandlastelementinsortedarray.py b/Python/firstandlastelementinsortedarray.py
new file mode 100644
index 0000000..2b7423b
--- /dev/null
+++ b/Python/firstandlastelementinsortedarray.py
@@ -0,0 +1,50 @@
+"""
+Given an array of integers nums sorted in ascending order, find the starting and ending position of a given target value.
+
+If target is not found in the array, return [-1, -1].
+
+You must write an algorithm with O(log n) runtime complexity.
+
+ 
+
+Example 1:
+
+Input: nums = [5,7,7,8,8,10], target = 8
+Output: [3,4]
+Example 2:
+
+Input: nums = [5,7,7,8,8,10], target = 6
+Output: [-1,-1]
+Example 3:
+
+Input: nums = [], target = 0
+Output: [-1,-1]
+ 
+
+Constraints:
+
+0 <= nums.length <= 105
+-109 <= nums[i] <= 109
+nums is a non-decreasing array.
+-109 <= target <= 109
+"""
+
+class Solution:
+    def searchRange(self, nums: List[int], target: int) -> List[int]:
+        if not nums:
+            return [-1,-1]
+        left, right = 0, len(nums)-1
+        while left <= right:
+            mid = (left+right)//2
+            if nums[mid] == target:
+                left, right = mid, mid
+                while left-1 >= 0 and nums[left-1] == target:
+                    left -= 1
+                while right+1 <= len(nums)-1 and nums[right+1] == target:
+                    right += 1
+                return [left, right]
+            elif nums[mid] < target:
+                left = mid+1
+            else:
+                right = mid-1
+        return [-1,-1]
\ No newline at end of file
diff --git a/Python/foursum.py b/Python/foursum.py
new file mode 100644
index 0000000..fe1aaa7
--- /dev/null
+++ b/Python/foursum.py
@@ -0,0 +1,53 @@
+'''
+Given an array nums of n integers, return an array of all the unique quadruplets [nums[a], nums[b], nums[c], nums[d]] such that:
+
+0 <= a, b, c, d < n
+a, b, c, and d are distinct.
+nums[a] + nums[b] + nums[c] + nums[d] == target
+You may return the answer in any order.
+
+ 
+
+Example 1:
+
+Input: nums = [1,0,-1,0,-2,2], target = 0
+Output: [[-2,-1,1,2],[-2,0,0,2],[-1,0,0,1]]
+Example 2:
+
+Input: nums = [2,2,2,2,2], target = 8
+Output: [[2,2,2,2]]
+ 
+
+Constraints:
+
+1 <= nums.length <= 200
+-109 <= nums[i] <= 109
+-109 <= target <= 109
+'''
+
+class Solution:
+    def fourSum(self, nums: List[int], target: int) -> List[List[int]]:
+        nums.sort()
+        res = []
+        for i in range(len(nums)-3):
+            if i > 0 and nums[i] == nums[i-1]:
+                continue
+            for j in range(i+1, len(nums)-2):
+                if j > i+1 and nums[j] == nums[j-1]:
+                    continue
+                l, r = j+1, len(nums)-1
+                while l < r:
+                    s = nums[i] + nums[j] + nums[l] + nums[r]
+                    if s == target:
+                        res.append([nums[i], nums[j], nums[l], nums[r]])
+                        l += 1
+                        r -= 1
+                        while l < r and nums[l] == nums[l-1]:
+                            l += 1
+                        while l < r and nums[r] == nums[r+1]:
+                            r -= 1
+                    elif s < target:
+                        l += 1
+                    else:
+                        r -= 1
+        return res
\ No newline at end of file
diff --git a/Python/generateparanthesis.py b/Python/generateparanthesis.py
new file mode 100644
index 0000000..3544eb1
--- /dev/null
+++ b/Python/generateparanthesis.py
@@ -0,0 +1,29 @@
+"""
+Given n pairs of parentheses, write a function to generate all combinations of well-formed parentheses.
+
+Example 1:
+
+Input: n = 3
+Output: ["((()))","(()())","(())()","()(())","()()()"]
+Example 2:
+
+Input: n = 1
+Output: ["()"]
+ 
+
+Constraints:
+
+1 <= n <= 8
+"""
+
+class Solution:
+    def generateParenthesis(self, n: int) -> List[str]:
+        def generate(p, left, right, parens=[]):
+            if left:
+                generate(p + '(', left - 1, right)
+            if right > left:
+                generate(p + ')', left, right - 1)
+            if not right:
+                parens += p,
+            return parens
+        return generate('', n, n)
\ No newline at end of file
diff --git a/Python/lowestcommonancestor.py b/Python/lowestcommonancestor.py
new file mode 100644
index 0000000..ae1606d
--- /dev/null
+++ b/Python/lowestcommonancestor.py
@@ -0,0 +1,55 @@
+Given a binary search tree (BST), find the lowest common ancestor (LCA) of two given nodes in the BST.
+
+According to the definition of LCA on Wikipedia: “The lowest common ancestor is defined between two nodes p and q as the lowest node in T that has both p and q as descendants (where we allow a node to be a descendant of itself).”
+
+ 
+
+Example 1:
+
+
+Input: root = [6,2,8,0,4,7,9,null,null,3,5], p = 2, q = 8
+Output: 6
+Explanation: The LCA of nodes 2 and 8 is 6.
+Example 2:
+
+
+# Input: root = [6,2,8,0,4,7,9,null,null,3,5], p = 2, q = 4
+# Output: 2
+# Explanation: The LCA of nodes 2 and 4 is 2, since a node can be a descendant of itself according to the LCA definition.
+# Example 3:
+
+# Input: root = [2,1], p = 2, q = 1
+# Output: 2
+ 
+
+# Constraints:
+
+# The number of nodes in the tree is in the range [2, 105].
+# -109 <= Node.val <= 109
+# All Node.val are unique.
+# p != q
+# p and q will exist in the BST.
+
+# Definition for a binary tree node.
+class TreeNode:
+    def __init__(self, x):
+        self.val = x
+        self.left = None
+        self.right = None
+
+class Solution:
+    def lowestCommonAncestor(self, root: TreeNode, p: TreeNode, q: TreeNode) -> TreeNode:
+        if not root:
+            return None
+        if root == p or root == q:
+            return root
+        left = self.lowestCommonAncestor(root.left, p, q)
+        right = self.lowestCommonAncestor(root.right, p, q)
+        if left and right:
+            return root
+        if left:
+            return left
+        if right:
+            return right
+        return None
+        
diff --git a/Python/mergeKLL.py b/Python/mergeKLL.py
new file mode 100644
index 0000000..cdfbce3
--- /dev/null
+++ b/Python/mergeKLL.py
@@ -0,0 +1,71 @@
+"""
+You are given an array of k linked-lists lists, each linked-list is sorted in ascending order.
+
+Merge all the linked-lists into one sorted linked-list and return it.
+
+ 
+
+Example 1:
+
+Input: lists = [[1,4,5],[1,3,4],[2,6]]
+Output: [1,1,2,3,4,4,5,6]
+Explanation: The linked-lists are:
+[
+  1->4->5,
+  1->3->4,
+  2->6
+]
+merging them into one sorted list:
+1->1->2->3->4->4->5->6
+Example 2:
+
+Input: lists = []
+Output: []
+Example 3:
+
+Input: lists = [[]]
+Output: []
+ 
+
+Constraints:
+
+k == lists.length
+0 <= k <= 10^4
+0 <= lists[i].length <= 500
+-10^4 <= lists[i][j] <= 10^4
+lists[i] is sorted in ascending order.
+The sum of lists[i].length won't exceed 10^4.
+"""
+
+Definition for singly-linked list.
+class ListNode:
+    def __init__(self, val=0, next=None):
+        self.val = val
+        self.next = next
+        
+class Solution:
+    def merge(left, right):
+        dummy = ListNode(0)
+        head = dummy
+        while left and right:
+            if left.val < right.val:
+                head.next = left
+                left = left.next
+            else:
+                head.next = right
+                right = right.next
+            head = head.next
+        if left:
+            head.next = left
+        if right:
+            head.next = right
+        return dummy.next
+    def mergeKLists(self, lists: List[ListNode]) -> ListNode:
+        if not lists:
+            return None
+        if len(lists) == 1:
+            return lists[0]
+        mid = len(lists) // 2
+        left = self.mergeKLists(lists[:mid])
+        right = self.mergeKLists(lists[mid:])
+        return self.merge(left, right)
\ No newline at end of file
diff --git a/Python/mergeLL.py b/Python/mergeLL.py
new file mode 100644
index 0000000..4ebfd05
--- /dev/null
+++ b/Python/mergeLL.py
@@ -0,0 +1,43 @@
+"""
+Merge two sorted linked lists and return it as a sorted list. The list should be made by splicing together the nodes of the first two lists.
+
+ 
+
+Example 1:
+
+
+Input: l1 = [1,2,4], l2 = [1,3,4]
+Output: [1,1,2,3,4,4]
+Example 2:
+
+Input: l1 = [], l2 = []
+Output: []
+Example 3:
+
+Input: l1 = [], l2 = [0]
+Output: [0]
+ 
+
+Constraints:
+
+The number of nodes in both lists is in the range [0, 50].
+-100 <= Node.val <= 100
+Both l1 and l2 are sorted in non-decreasing order.
+"""
+# Definition for singly-linked list.
+class ListNode:
+    def __init__(self, val=0, next=None):
+        self.val = val
+        self.next = next
+class Solution:
+    def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode:
+        if l1 is None:
+            return l2
+        if l2 is None:
+            return l1
+        if l1.val < l2.val:
+            l1.next = self.mergeTwoLists(l1.next, l2)
+            return l1
+        else:
+            l2.next = self.mergeTwoLists(l1, l2.next)
+            return l2
\ No newline at end of file
diff --git a/Python/partitiondisjoint.py b/Python/partitiondisjoint.py
new file mode 100644
index 0000000..d32243a
--- /dev/null
+++ b/Python/partitiondisjoint.py
@@ -0,0 +1,45 @@
+"""
+Given an array nums, partition it into two (contiguous) subarrays left and right so that:
+
+Every element in left is less than or equal to every element in right.
+left and right are non-empty.
+left has the smallest possible size.
+Return the length of left after such a partitioning.  It is guaranteed that such a partitioning exists.
+
+ 
+
+Example 1:
+
+Input: nums = [5,0,3,8,6]
+Output: 3
+Explanation: left = [5,0,3], right = [8,6]
+Example 2:
+
+Input: nums = [1,1,1,0,6,12]
+Output: 4
+Explanation: left = [1,1,1,0], right = [6,12]
+ 
+
+Note:
+
+2 <= nums.length <= 30000
+0 <= nums[i] <= 106
+It is guaranteed there is at least one way to partition nums as described.
+        
+"""
+
+class Solution:
+    def partitionDisjoint(self, nums: List[int]) -> int:
+        n = len(nums)
+        if n == 0: return 0
+        if n == 1: return 1
+        left_max = [nums[0]]
+        right_max = [nums[-1]]
+        for i in range(1, n):
+            left_max.append(max(left_max[-1], nums[i]))
+        for i in range(n-2, -1, -1):
+            right_max.insert(0, min(right_max[0], nums[i]))
+        for i in range(n-1):
+            if left_max[i] <= right_max[i+1]:
+                return i+1
+        return n
\ No newline at end of file
diff --git a/Python/searchhinsortedarray.py b/Python/searchhinsortedarray.py
new file mode 100644
index 0000000..ad0c1ef
--- /dev/null
+++ b/Python/searchhinsortedarray.py
@@ -0,0 +1,10 @@
+
+
+
+from collections import Counter
+
+n = (int(input()))
+x = (int(input()))
+y = list(map(int, input().split()))
+
+
diff --git a/Python/sudoku_solver.py b/Python/sudoku_solver.py
new file mode 100644
index 0000000..7f0ad05
--- /dev/null
+++ b/Python/sudoku_solver.py
@@ -0,0 +1,63 @@
+"""
+Write a program to solve a Sudoku puzzle by filling the empty cells.
+
+A sudoku solution must satisfy all of the following rules:
+
+Each of the digits 1-9 must occur exactly once in each row.
+Each of the digits 1-9 must occur exactly once in each column.
+Each of the digits 1-9 must occur exactly once in each of the 9 3x3 sub-boxes of the grid.
+The '.' character indicates empty cells.
+
+ 
+
+Example 1:
+
+
+Input: board = [["5","3",".",".","7",".",".",".","."],["6",".",".","1","9","5",".",".","."],[".","9","8",".",".",".",".","6","."],["8",".",".",".","6",".",".",".","3"],["4",".",".","8",".","3",".",".","1"],["7",".",".",".","2",".",".",".","6"],[".","6",".",".",".",".","2","8","."],[".",".",".","4","1","9",".",".","5"],[".",".",".",".","8",".",".","7","9"]]
+Output: [["5","3","4","6","7","8","9","1","2"],["6","7","2","1","9","5","3","4","8"],["1","9","8","3","4","2","5","6","7"],["8","5","9","7","6","1","4","2","3"],["4","2","6","8","5","3","7","9","1"],["7","1","3","9","2","4","8","5","6"],["9","6","1","5","3","7","2","8","4"],["2","8","7","4","1","9","6","3","5"],["3","4","5","2","8","6","1","7","9"]]
+Explanation: The input board is shown above and the only valid solution is shown below:
+
+
+ 
+
+Constraints:
+
+board.length == 9
+board[i].length == 9
+board[i][j] is a digit or '.'.
+It is guaranteed that the input board has only one solution.
+
+"""
+
+class Solution:
+    def solve(self, board):
+        for i in range(9):
+            for j in range(9):
+                if board[i][j] == '.':
+                    for k in range(1, 10):
+                        if self.isValid(board, i, j, str(k)):
+                            board[i][j] = str(k)
+                            if self.solve(board):
+                                return True
+                            else:
+                                board[i][j] = '.'
+                    return False
+        return True
+
+
+    def solveSudoku(self, board: List[List[str]]) -> None:
+        """
+        Do not return anything, modify board in-place instead.
+        """
+        self.solve(board)
+
+    def isValid(self, board, i, j, k):
+        for m in range(9):
+            if board[m][j] == k:
+                return False
+            if board[i][m] == k:
+                return False
+            if board[3*(i//3)+m//3][3*(j//3)+m%3] == k:
+                return False
+        return True
+        
\ No newline at end of file
diff --git a/Python/sumequalszero.py b/Python/sumequalszero.py
new file mode 100644
index 0000000..d9ae2ca
--- /dev/null
+++ b/Python/sumequalszero.py
@@ -0,0 +1,55 @@
+# Given an integer array nums, return all the triplets [nums[i], nums[j], nums[k]] such that i != j, i != k, and j != k, and nums[i] + nums[j] + nums[k] == 0.
+
+# Notice that the solution set must not contain duplicate triplets.
+
+ 
+
+# Example 1:
+
+# Input: nums = [-1,0,1,2,-1,-4]
+# Output: [[-1,-1,2],[-1,0,1]]
+# Example 2:
+
+# Input: nums = []
+# Output: []
+# Example 3:
+
+# Input: nums = [0]
+# Output: []
+ 
+
+# Constraints:
+
+# 0 <= nums.length <= 3000
+# -105 <= nums[i] <= 105
+# Accepted
+# 1,388,410
+# Submissions
+# 4,799,808
+
+
+class Solution:
+    def threeSum(self, nums: List[int]) -> List[List[int]]:
+        if len(nums) < 3:
+            return []
+        nums.sort()
+        res = []
+        for i in range(len(nums) - 2):
+            if i > 0 and nums[i] == nums[i - 1]:
+                continue
+            l, r = i + 1, len(nums) - 1
+            while l < r:
+                s = nums[i] + nums[l] + nums[r]
+                if s < 0:
+                    l += 1
+                elif s > 0:
+                    r -= 1
+                else:
+                    res.append([nums[i], nums[l], nums[r]])
+                    while l < r and nums[l] == nums[l + 1]:
+                        l += 1
+                    while l < r and nums[r] == nums[r - 1]:
+                        r -= 1
+                    l += 1
+                    r -= 1
+        return res
\ No newline at end of file
diff --git a/Python/threesumclosest.py b/Python/threesumclosest.py
new file mode 100644
index 0000000..b01af74
--- /dev/null
+++ b/Python/threesumclosest.py
@@ -0,0 +1,34 @@
+# Given an array nums of n integers and an integer target, find three integers in nums such that the sum is closest to target. Return the sum of the three integers. You may assume that each input would have exactly one solution.
+
+ 
+
+# Example 1:
+
+# Input: nums = [-1,2,1,-4], target = 1
+# Output: 2
+# Explanation: The sum that is closest to the target is 2. (-1 + 2 + 1 = 2).
+ 
+
+# Constraints:
+
+# 3 <= nums.length <= 10^3
+# -10^3 <= nums[i] <= 10^3
+# -10^4 <= target <= 10^4
+
+class Solution:
+    def threeSumClosest(self, nums: List[int], target: int) -> int:
+        nums.sort()
+        res = nums[0] + nums[1] + nums[2]
+        for i in range(len(nums) - 2):
+            j, k = i+1, len(nums) - 1
+            while j < k:
+                sum = nums[i] + nums[j] + nums[k]
+                if sum == target:
+                    return sum
+                if abs(sum - target) < abs(res - target):
+                    res = sum
+                if sum < target:
+                    j += 1
+                elif sum > target:
+                    k -= 1
+        return res
\ No newline at end of file

From 9616d6c455b1a7124f84587ab09c892815fab1f7 Mon Sep 17 00:00:00 2001
From: QuratAin <quratulainshoro1@outlook.com>
Date: Sun, 31 Oct 2021 14:17:39 +0500
Subject: [PATCH 158/232] deleed

---
 Javascript/JSCharts.html | 47 ----------------------------------------
 1 file changed, 47 deletions(-)
 delete mode 100644 Javascript/JSCharts.html

diff --git a/Javascript/JSCharts.html b/Javascript/JSCharts.html
deleted file mode 100644
index 787d6cc..0000000
--- a/Javascript/JSCharts.html
+++ /dev/null
@@ -1,47 +0,0 @@
-<!DOCTYPE html>
-<html>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/2.9.4/Chart.js"></script>
-<script src="Charts.js"></script>
-<title>Chart.js</title>
-<body>
-<h2>Chart.js comes with built-in chart types.</h2>
-<h3>Following is the example of a Scatter Plot</h3><br>
-<canvas id="myChart" style="width:100%;max-width:700px"></canvas>
-
-<script>
-var xyValues = [
-  {x:50, y:7},
-  {x:60, y:8},
-  {x:70, y:8},
-  {x:80, y:9},
-  {x:90, y:9},
-  {x:100, y:9},
-  {x:110, y:10},
-  {x:120, y:11},
-  {x:130, y:14},
-  {x:140, y:14},
-  {x:150, y:15}
-];
-
-new Chart("myChart", {
-  type: "scatter",
-  data: {
-    datasets: [{
-      pointRadius: 4,
-      pointBackgroundColor: "rgb(0,0,255)",
-      data: xyValues
-    }]
-  },
-  options: {
-    legend: {display: false},
-    scales: {
-      xAxes: [{ticks: {min: 40, max:160}}],
-      yAxes: [{ticks: {min: 6, max:16}}],
-    }
-  }
-});
-</script>
-</body>
-</html>
-
-

From e824ea0558b170495011ba25a779b51bb148c6fa Mon Sep 17 00:00:00 2001
From: QuratAin <quratulainshoro1@outlook.com>
Date: Sun, 31 Oct 2021 14:20:30 +0500
Subject: [PATCH 159/232] Added code to create graphs using chart.js

---
 jsChart.html | 47 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 47 insertions(+)
 create mode 100644 jsChart.html

diff --git a/jsChart.html b/jsChart.html
new file mode 100644
index 0000000..787d6cc
--- /dev/null
+++ b/jsChart.html
@@ -0,0 +1,47 @@
+<!DOCTYPE html>
+<html>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/Chart.js/2.9.4/Chart.js"></script>
+<script src="Charts.js"></script>
+<title>Chart.js</title>
+<body>
+<h2>Chart.js comes with built-in chart types.</h2>
+<h3>Following is the example of a Scatter Plot</h3><br>
+<canvas id="myChart" style="width:100%;max-width:700px"></canvas>
+
+<script>
+var xyValues = [
+  {x:50, y:7},
+  {x:60, y:8},
+  {x:70, y:8},
+  {x:80, y:9},
+  {x:90, y:9},
+  {x:100, y:9},
+  {x:110, y:10},
+  {x:120, y:11},
+  {x:130, y:14},
+  {x:140, y:14},
+  {x:150, y:15}
+];
+
+new Chart("myChart", {
+  type: "scatter",
+  data: {
+    datasets: [{
+      pointRadius: 4,
+      pointBackgroundColor: "rgb(0,0,255)",
+      data: xyValues
+    }]
+  },
+  options: {
+    legend: {display: false},
+    scales: {
+      xAxes: [{ticks: {min: 40, max:160}}],
+      yAxes: [{ticks: {min: 6, max:16}}],
+    }
+  }
+});
+</script>
+</body>
+</html>
+
+

From ebfc4dca482b2f91fa787704f8dfe9a2c6f59719 Mon Sep 17 00:00:00 2001
From: QuratAin <quratulainshoro1@outlook.com>
Date: Sun, 31 Oct 2021 14:26:34 +0500
Subject: [PATCH 160/232] Html Js DOM Animation

---
 Javascript/{Animationsindex.html => jsAnimations.html} | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename Javascript/{Animationsindex.html => jsAnimations.html} (100%)

diff --git a/Javascript/Animationsindex.html b/Javascript/jsAnimations.html
similarity index 100%
rename from Javascript/Animationsindex.html
rename to Javascript/jsAnimations.html

From e642f555894a65973e12db5e372b394d765bc818 Mon Sep 17 00:00:00 2001
From: QuratAin <quratulainshoro1@outlook.com>
Date: Sun, 31 Oct 2021 14:27:59 +0500
Subject: [PATCH 161/232] js animation

---
 Javascript/jsAnimations.html | 32 --------------------------------
 1 file changed, 32 deletions(-)

diff --git a/Javascript/jsAnimations.html b/Javascript/jsAnimations.html
index 52bb83e..ab927a0 100644
--- a/Javascript/jsAnimations.html
+++ b/Javascript/jsAnimations.html
@@ -1,35 +1,3 @@
-<<<<<<< HEAD
-<!DOCTYPE html>
-<html>
-<head>
-    <meta charset="UTF-8">
-    <meta http-equiv="X-UA-Compatible" content="IE=edge">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <script src="Animations.js"></script>
-    <title>Javascript HTML BASic DOM Animations</title>
-    <style>
-        #Container{
-            width: 400px;
-            height: 400px;
-            position: relative;
-            background: orangered;
-        }
-        #animationBox{
-            width: 50px;
-            height: 50px;
-            position: absolute;
-            background-color: yellow;
-        }
-    </style>
-</head>
-<body>
-    <p><button onclick="myAnimation()">Click to Animate the box</button></p>
-    <div id="Container">
-        <div id="animationBox">
-        </div>
-    </div>
-</body>
-=======
 <!DOCTYPE html>
 <html lang="en">
 <head>

From 02ee760fbd6f747f4408c04dd312a6861e741767 Mon Sep 17 00:00:00 2001
From: QuratAin <quratulainshoro1@outlook.com>
Date: Sun, 31 Oct 2021 14:37:14 +0500
Subject: [PATCH 162/232] deleted

---
 Javascript/Animations.js     | 38 ------------------------------------
 Javascript/index.html        | 31 -----------------------------
 Javascript/jsAnimations.html | 32 ------------------------------
 3 files changed, 101 deletions(-)
 delete mode 100644 Javascript/Animations.js
 delete mode 100644 Javascript/index.html
 delete mode 100644 Javascript/jsAnimations.html

diff --git a/Javascript/Animations.js b/Javascript/Animations.js
deleted file mode 100644
index c3322bc..0000000
--- a/Javascript/Animations.js
+++ /dev/null
@@ -1,38 +0,0 @@
-<<<<<<< HEAD
-function myAnimation(){
-    let id = null;
-    const box = document.getElementById("animationBox");
-    let pos = 0;
-    clearInterval(id);
-    id = setInterval(frame, 5);
-    
-    function frame(){
-        if(pos == 350){
-            clearInterval(id);
-        }
-        else{
-            pos++;
-            box.style.top = pos + "px";
-            box.style.left = pos + "px";
-        }
-    }
-=======
-function myAnimation(){
-    let id = null;
-    const box = document.getElementById("animationBox");
-    let pos = 0;
-    clearInterval(id);
-    id = setInterval(frame, 5);
-    
-    function frame(){
-        if(pos == 350){
-            clearInterval(id);
-        }
-        else{
-            pos++;
-            box.style.top = pos + "px";
-            box.style.left = pos + "px";
-        }
-    }
->>>>>>> b87e6665d167e831a72ed8e150a2a3767ac3ac10
-}
\ No newline at end of file
diff --git a/Javascript/index.html b/Javascript/index.html
deleted file mode 100644
index 6a1dcc3..0000000
--- a/Javascript/index.html
+++ /dev/null
@@ -1,31 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta http-equiv="X-UA-Compatible" content="IE=edge">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <script src="Animations.js"></script>
-    <title>Javascript HTML DOM Animations</title>
-    <style>
-        #Container{
-            width: 400px;
-            height: 400px;
-            position: relative;
-            background: orangered;
-        }
-        #animationBox{
-            width: 50px;
-            height: 50px;
-            position: absolute;
-            background-color: yellow;
-        }
-    </style>
-</head>
-<body>
-    <p><button onclick="myAnimation()">Click to Animate</button></p>
-    <div id="Container">
-        <div id="animationBox">
-        </div>
-    </div>
-</body>
-</html>
\ No newline at end of file
diff --git a/Javascript/jsAnimations.html b/Javascript/jsAnimations.html
deleted file mode 100644
index ab927a0..0000000
--- a/Javascript/jsAnimations.html
+++ /dev/null
@@ -1,32 +0,0 @@
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta http-equiv="X-UA-Compatible" content="IE=edge">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <script src="Animations.js"></script>
-    <title>Javascript HTML DOM Animations</title>
-    <style>
-        #Container{
-            width: 400px;
-            height: 400px;
-            position: relative;
-            background: orangered;
-        }
-        #animationBox{
-            width: 50px;
-            height: 50px;
-            position: absolute;
-            background-color: yellow;
-        }
-    </style>
-</head>
-<body>
-    <p><button onclick="myAnimation()">Click to Animate</button></p>
-    <div id="Container">
-        <div id="animationBox">
-        </div>
-    </div>
-</body>
->>>>>>> b87e6665d167e831a72ed8e150a2a3767ac3ac10
-</html>
\ No newline at end of file

From 8a901fefadbb2bedb260c5d22a3cbfa32ed3bdde Mon Sep 17 00:00:00 2001
From: QuratAin <quratulainshoro1@outlook.com>
Date: Sun, 31 Oct 2021 14:49:27 +0500
Subject: [PATCH 163/232] Added HTML JS DOM Animation

---
 Javascript/jsDOMAnimations.html | 48 +++++++++++++++++++++++++++++++++
 1 file changed, 48 insertions(+)
 create mode 100644 Javascript/jsDOMAnimations.html

diff --git a/Javascript/jsDOMAnimations.html b/Javascript/jsDOMAnimations.html
new file mode 100644
index 0000000..694d60d
--- /dev/null
+++ b/Javascript/jsDOMAnimations.html
@@ -0,0 +1,48 @@
+<!DOCTYPE html>
+<html>
+<head>
+    <title>Js Animations</title>
+</head>
+<style>
+#container {
+  width: 400px;
+  height: 400px;
+  position: relative;
+  background: yellow;
+}
+#animate {
+  width: 50px;
+  height: 50px;
+  position: absolute;
+  background-color: red;
+}
+</style>
+<body>
+
+<p><button onclick="myMove()">Click to Animate</button></p> 
+
+<div id ="container">
+  <div id ="animate"></div>
+</div>
+
+<script>
+function myMove() {
+  let id = null;
+  const elem = document.getElementById("animate");   
+  let pos = 0;
+  clearInterval(id);
+  id = setInterval(frame, 5);
+  function frame() {
+    if (pos == 350) {
+      clearInterval(id);
+    } else {
+      pos++; 
+      elem.style.top = pos + "px"; 
+      elem.style.left = pos + "px"; 
+    }
+  }
+}
+</script>
+
+</body>
+</html>

From b3f91e949e6a18a37b5346a69d0f40b672652486 Mon Sep 17 00:00:00 2001
From: QuratAin <quratulainshoro1@outlook.com>
Date: Sun, 31 Oct 2021 15:01:12 +0500
Subject: [PATCH 164/232] Html Js DOM Animation

---
 Javascript/jsDOMAnimations.html | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Javascript/jsDOMAnimations.html b/Javascript/jsDOMAnimations.html
index 694d60d..f86aac0 100644
--- a/Javascript/jsDOMAnimations.html
+++ b/Javascript/jsDOMAnimations.html
@@ -19,7 +19,7 @@
 </style>
 <body>
 
-<p><button onclick="myMove()">Click to Animate</button></p> 
+<p><button onclick="myMove()">Click to Animate</button></p> <br>
 
 <div id ="container">
   <div id ="animate"></div>

From 28845b3c583deea9802bbabeb573466ff524d9ec Mon Sep 17 00:00:00 2001
From: Qurat Ul Ain Shoro <65081454+QuratAin@users.noreply.github.com>
Date: Sun, 31 Oct 2021 15:26:41 +0500
Subject: [PATCH 165/232] Create jsAnimations.html

---
 jsAnimations.html | 48 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 48 insertions(+)
 create mode 100644 jsAnimations.html

diff --git a/jsAnimations.html b/jsAnimations.html
new file mode 100644
index 0000000..e6485ae
--- /dev/null
+++ b/jsAnimations.html
@@ -0,0 +1,48 @@
+!DOCTYPE html>
+<html>
+<head>
+    <title>Js Animations</title>
+</head>
+<style>
+#container {
+  width: 400px;
+  height: 400px;
+  position: relative;
+  background: yellow;
+}
+#animate {
+  width: 50px;
+  height: 50px;
+  position: absolute;
+  background-color: red;
+}
+</style>
+<body>
+
+<p><button onclick="myMove()">Click to Animate</button></p> <br>
+
+<div id ="container">
+  <div id ="animate"></div>
+</div>
+
+<script>
+function myMove() {
+  let id = null;
+  const elem = document.getElementById("animate");   
+  let pos = 0;
+  clearInterval(id);
+  id = setInterval(frame, 5);
+  function frame() {
+    if (pos == 350) {
+      clearInterval(id);
+    } else {
+      pos++; 
+      elem.style.top = pos + "px"; 
+      elem.style.left = pos + "px"; 
+    }
+  }
+}
+</script>
+
+</body>
+</html>

From 8db83c2eabd8fc9e3d5dba36a656dae8318f9334 Mon Sep 17 00:00:00 2001
From: Mursal Furqan Kumbhar <mursalfurqan@gmail.com>
Date: Wed, 1 Oct 2025 08:58:37 +0200
Subject: [PATCH 166/232] Implement iterative binary search function

---
 C#/Searching/BinarySearch(Iterative).cs | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)
 create mode 100644 C#/Searching/BinarySearch(Iterative).cs

diff --git a/C#/Searching/BinarySearch(Iterative).cs b/C#/Searching/BinarySearch(Iterative).cs
new file mode 100644
index 0000000..279232c
--- /dev/null
+++ b/C#/Searching/BinarySearch(Iterative).cs
@@ -0,0 +1,19 @@
+int BinarySearchIterative(int[] arr, int target)
+{
+    int left = 0, right = arr.Length - 1;
+
+    while (left <= right)
+    {
+        int mid = left + (right - left) / 2;
+
+        if (arr[mid] == target)
+            return mid; // Found
+
+        if (arr[mid] < target)
+            left = mid + 1; // Search right half
+        else
+            right = mid - 1; // Search left half
+    }
+
+    return -1; // Not found
+}

From 60d3a51512939902b83a58c3628aac30ab64c4af Mon Sep 17 00:00:00 2001
From: Mursal Furqan Kumbhar <mursalfurqan@gmail.com>
Date: Wed, 1 Oct 2025 09:02:15 +0200
Subject: [PATCH 167/232] Create BinarySearch(Recursive).cs

---
 C#/Searching/BinarySearch(Recursive).cs | 15 +++++++++++++++
 1 file changed, 15 insertions(+)
 create mode 100644 C#/Searching/BinarySearch(Recursive).cs

diff --git a/C#/Searching/BinarySearch(Recursive).cs b/C#/Searching/BinarySearch(Recursive).cs
new file mode 100644
index 0000000..6a9c045
--- /dev/null
+++ b/C#/Searching/BinarySearch(Recursive).cs
@@ -0,0 +1,15 @@
+int BinarySearchRecursive(int[] arr, int left, int right, int target)
+{
+    if (left > right)
+        return -1;
+
+    int mid = left + (right - left) / 2;
+
+    if (arr[mid] == target)
+        return mid;
+
+    if (arr[mid] < target)
+        return BinarySearchRecursive(arr, mid + 1, right, target);
+    else
+        return BinarySearchRecursive(arr, left, mid - 1, target);
+}

From 93318e6020b680cecec35547a427fb4940137396 Mon Sep 17 00:00:00 2001
From: Mursal Furqan Kumbhar <mursalfurqan@gmail.com>
Date: Wed, 1 Oct 2025 09:02:25 +0200
Subject: [PATCH 168/232] Create JumpSearch.cs

---
 C#/Searching/JumpSearch.cs | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)
 create mode 100644 C#/Searching/JumpSearch.cs

diff --git a/C#/Searching/JumpSearch.cs b/C#/Searching/JumpSearch.cs
new file mode 100644
index 0000000..190d3cc
--- /dev/null
+++ b/C#/Searching/JumpSearch.cs
@@ -0,0 +1,27 @@
+using System;
+
+int JumpSearch(int[] arr, int target)
+{
+    int n = arr.Length;
+    int step = (int)Math.Floor(Math.Sqrt(n));
+    int prev = 0;
+
+    // Jump until we find a block that may contain the element
+    while (prev < n && arr[Math.Min(step, n) - 1] < target)
+    {
+        prev = step;
+        step += (int)Math.Floor(Math.Sqrt(n));
+
+        if (prev >= n)
+            return -1;
+    }
+
+    // Linear search within the block
+    for (int i = prev; i < Math.Min(step, n); i++)
+    {
+        if (arr[i] == target)
+            return i;
+    }
+
+    return -1;
+}

From 78b5e223181f9aaf2424db791c871809d1308378 Mon Sep 17 00:00:00 2001
From: Mursal Furqan Kumbhar <mursalfurqan@gmail.com>
Date: Wed, 1 Oct 2025 09:02:29 +0200
Subject: [PATCH 169/232] Create InterpolationSearch.cs

---
 C#/Searching/InterpolationSearch.cs | 25 +++++++++++++++++++++++++
 1 file changed, 25 insertions(+)
 create mode 100644 C#/Searching/InterpolationSearch.cs

diff --git a/C#/Searching/InterpolationSearch.cs b/C#/Searching/InterpolationSearch.cs
new file mode 100644
index 0000000..4311a3f
--- /dev/null
+++ b/C#/Searching/InterpolationSearch.cs
@@ -0,0 +1,25 @@
+int InterpolationSearch(int[] arr, int target)
+{
+    int low = 0, high = arr.Length - 1;
+
+    while (low <= high && target >= arr[low] && target <= arr[high])
+    {
+        if (low == high)
+        {
+            return arr[low] == target ? low : -1;
+        }
+
+        // Estimate the position
+        int pos = low + ((target - arr[low]) * (high - low)) / (arr[high] - arr[low]);
+
+        if (arr[pos] == target)
+            return pos;
+
+        if (arr[pos] < target)
+            low = pos + 1;
+        else
+            high = pos - 1;
+    }
+
+    return -1;
+}

From c5ebc1b4872b661b8ee234c3f775de62ae6fe03f Mon Sep 17 00:00:00 2001
From: Mursal Furqan Kumbhar <mursalfurqan@gmail.com>
Date: Wed, 1 Oct 2025 09:02:34 +0200
Subject: [PATCH 170/232] Create ExponentialSearch.cs

---
 C#/Searching/ExponentialSearch.cs | 14 ++++++++++++++
 1 file changed, 14 insertions(+)
 create mode 100644 C#/Searching/ExponentialSearch.cs

diff --git a/C#/Searching/ExponentialSearch.cs b/C#/Searching/ExponentialSearch.cs
new file mode 100644
index 0000000..fdc7967
--- /dev/null
+++ b/C#/Searching/ExponentialSearch.cs
@@ -0,0 +1,14 @@
+int ExponentialSearch(int[] arr, int target)
+{
+    int n = arr.Length;
+
+    if (arr[0] == target)
+        return 0;
+
+    int i = 1;
+    while (i < n && arr[i] <= target)
+        i *= 2;
+
+    // Binary search in found range
+    return BinarySearchRecursive(arr, i / 2, Math.Min(i, n - 1), target);
+}

From 7986cba996b975c8e11d6c9bd4160ece516821bd Mon Sep 17 00:00:00 2001
From: Mursal Furqan Kumbhar <mursalfurqan@gmail.com>
Date: Wed, 1 Oct 2025 09:02:39 +0200
Subject: [PATCH 171/232] Implement Fibonacci search algorithm

---
 C#/Searching/FibonacciSearch.cs | 47 +++++++++++++++++++++++++++++++++
 1 file changed, 47 insertions(+)
 create mode 100644 C#/Searching/FibonacciSearch.cs

diff --git a/C#/Searching/FibonacciSearch.cs b/C#/Searching/FibonacciSearch.cs
new file mode 100644
index 0000000..cbd4153
--- /dev/null
+++ b/C#/Searching/FibonacciSearch.cs
@@ -0,0 +1,47 @@
+int FibonacciSearch(int[] arr, int target)
+{
+    int n = arr.Length;
+
+    int fibMMm2 = 0;  // (m-2)’th Fibonacci number
+    int fibMMm1 = 1;  // (m-1)’th Fibonacci number
+    int fibM = fibMMm2 + fibMMm1; // m’th Fibonacci number
+
+    // Find smallest Fibonacci number >= n
+    while (fibM < n)
+    {
+        fibMMm2 = fibMMm1;
+        fibMMm1 = fibM;
+        fibM = fibMMm2 + fibMMm1;
+    }
+
+    int offset = -1;
+
+    while (fibM > 1)
+    {
+        int i = Math.Min(offset + fibMMm2, n - 1);
+
+        if (arr[i] < target)
+        {
+            fibM = fibMMm1;
+            fibMMm1 = fibMMm2;
+            fibMMm2 = fibM - fibMMm1;
+            offset = i;
+        }
+        else if (arr[i] > target)
+        {
+            fibM = fibMMm2;
+            fibMMm1 = fibMMm1 - fibMMm2;
+            fibMMm2 = fibM - fibMMm1;
+        }
+        else
+        {
+            return i;
+        }
+    }
+
+    // Compare last element
+    if (fibMMm1 == 1 && arr[offset + 1] == target)
+        return offset + 1;
+
+    return -1;
+}

From d15519398968a41ef5f169cc9c2979fc61f432ff Mon Sep 17 00:00:00 2001
From: dilzaina99 <78306108+dilzaina99@users.noreply.github.com>
Date: Wed, 1 Oct 2025 20:34:50 +0200
Subject: [PATCH 172/232] =?UTF-8?q?Create=20Binary=20Search=20Tree=20(BST)?=
 =?UTF-8?q?=20=E2=80=94=20exact=20key=20lookup?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ... Tree (BST) \342\200\224 exact key lookup" | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)
 create mode 100644 "C#/Searching/Binary Search Tree (BST) \342\200\224 exact key lookup"

diff --git "a/C#/Searching/Binary Search Tree (BST) \342\200\224 exact key lookup" "b/C#/Searching/Binary Search Tree (BST) \342\200\224 exact key lookup"
new file mode 100644
index 0000000..a652cf1
--- /dev/null
+++ "b/C#/Searching/Binary Search Tree (BST) \342\200\224 exact key lookup"	
@@ -0,0 +1,19 @@
+public class BSTNode
+{
+    public int Key;
+    public BSTNode Left, Right;
+    public BSTNode(int key) => Key = key;
+}
+
+public static class BST
+{
+    public static BSTNode Search(BSTNode root, int key)
+    {
+        while (root != null)
+        {
+            if (key == root.Key) return root;
+            root = key < root.Key ? root.Left : root.Right;
+        }
+        return null;
+    }
+}

From c576fcf2011b48107a356028fc17ec5832d5123e Mon Sep 17 00:00:00 2001
From: dilzaina99 <78306108+dilzaina99@users.noreply.github.com>
Date: Wed, 1 Oct 2025 20:35:48 +0200
Subject: [PATCH 173/232] Create AVL Tree

---
 C#/Searching/AVL Tree | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)
 create mode 100644 C#/Searching/AVL Tree

diff --git a/C#/Searching/AVL Tree b/C#/Searching/AVL Tree
new file mode 100644
index 0000000..b1d5ed5
--- /dev/null
+++ b/C#/Searching/AVL Tree	
@@ -0,0 +1,19 @@
+public class AVLNode
+{
+    public int Key, Height = 1;
+    public AVLNode Left, Right;
+    public AVLNode(int key) => Key = key;
+}
+
+public static class AVL
+{
+    public static AVLNode Search(AVLNode root, int key)
+    {
+        while (root != null)
+        {
+            if (key == root.Key) return root;
+            root = key < root.Key ? root.Left : root.Right;
+        }
+        return null;
+    }
+}

From 8693520ff2baaae5adb0b4604b88e3343102aaf8 Mon Sep 17 00:00:00 2001
From: dilzaina99 <78306108+dilzaina99@users.noreply.github.com>
Date: Wed, 1 Oct 2025 20:36:29 +0200
Subject: [PATCH 174/232] Create Red-Black Tree

---
 Red-Black Tree | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 Red-Black Tree

diff --git a/Red-Black Tree b/Red-Black Tree
new file mode 100644
index 0000000..aaceb32
--- /dev/null
+++ b/Red-Black Tree	
@@ -0,0 +1,22 @@
+public enum Color { Red, Black }
+
+public class RBNode
+{
+    public int Key;
+    public Color Color;
+    public RBNode Left, Right, Parent;
+    public RBNode(int key, Color color = Color.Red) { Key = key; Color = color; }
+}
+
+public static class RedBlack
+{
+    public static RBNode Search(RBNode root, int key)
+    {
+        while (root != null)
+        {
+            if (key == root.Key) return root;
+            root = key < root.Key ? root.Left : root.Right;
+        }
+        return null;
+    }
+}

From 0e8fccf6a859bd4f07caf33cf553a5a391bfe134 Mon Sep 17 00:00:00 2001
From: dilzaina99 <78306108+dilzaina99@users.noreply.github.com>
Date: Wed, 1 Oct 2025 20:37:23 +0200
Subject: [PATCH 175/232] =?UTF-8?q?Create=20B-Tree=20=E2=80=94=20node=20or?=
 =?UTF-8?q?=20key=20search=20(t=20=3D=20minimum=20degree)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...4 node or key search (t = minimum degree)" | 22 +++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 "C#/Searching/B-Tree \342\200\224 node or key search (t = minimum degree)"

diff --git "a/C#/Searching/B-Tree \342\200\224 node or key search (t = minimum degree)" "b/C#/Searching/B-Tree \342\200\224 node or key search (t = minimum degree)"
new file mode 100644
index 0000000..61a3c2b
--- /dev/null
+++ "b/C#/Searching/B-Tree \342\200\224 node or key search (t = minimum degree)"	
@@ -0,0 +1,22 @@
+using System;
+using System.Collections.Generic;
+
+public class BTreeNode
+{
+    public int T;                 // min degree
+    public List<int> Keys = new();
+    public List<BTreeNode> Children = new(); // size = Keys.Count + 1 when not leaf
+    public bool IsLeaf => Children.Count == 0;
+
+    public BTreeNode(int t) => T = t;
+
+    // Returns tuple (node, index) if found, else (null, -1)
+    public (BTreeNode node, int index) Search(int key)
+    {
+        int i = Keys.BinarySearch(key);
+        if (i >= 0) return (this, i);
+        i = ~i; // insertion position
+        if (IsLeaf) return (null, -1);
+        return Children[i].Search(key);
+    }
+}

From c768dcda472cf02cbd06ba8bfebfb90421634c43 Mon Sep 17 00:00:00 2001
From: dilzaina99 <78306108+dilzaina99@users.noreply.github.com>
Date: Wed, 1 Oct 2025 20:37:53 +0200
Subject: [PATCH 176/232] =?UTF-8?q?Create=20B+=20Tree=20=E2=80=94=20search?=
 =?UTF-8?q?=20key=20in=20leaves=20(simplified=20leaf=20scan)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...arch key in leaves (simplified leaf scan)" | 30 +++++++++++++++++++
 1 file changed, 30 insertions(+)
 create mode 100644 "C#/Searching/B+ Tree \342\200\224 search key in leaves (simplified leaf scan)"

diff --git "a/C#/Searching/B+ Tree \342\200\224 search key in leaves (simplified leaf scan)" "b/C#/Searching/B+ Tree \342\200\224 search key in leaves (simplified leaf scan)"
new file mode 100644
index 0000000..2763435
--- /dev/null
+++ "b/C#/Searching/B+ Tree \342\200\224 search key in leaves (simplified leaf scan)"	
@@ -0,0 +1,30 @@
+using System.Collections.Generic;
+
+public class BPlusNode
+{
+    public bool IsLeaf;
+    public List<int> Keys = new();
+    public List<BPlusNode> Children = new(); // internal nodes only
+    public BPlusNode NextLeaf;               // leaf-level linked list
+}
+
+public static class BPlusTree
+{
+    // Descend to appropriate leaf, then scan keys
+    public static bool Search(BPlusNode root, int key)
+    {
+        if (root == null) return false;
+
+        var cur = root;
+        while (!cur.IsLeaf)
+        {
+            int i = cur.Keys.BinarySearch(key);
+            if (i >= 0) i++;     // go to right child on equal (typical B+ convention)
+            else i = ~i;         // first greater-than position
+            cur = cur.Children[i];
+        }
+
+        // At leaf: exact lookup
+        return cur.Keys.BinarySearch(key) >= 0;
+    }
+}

From f85e6ce197d640f76a36f2ab768727672d5b54b8 Mon Sep 17 00:00:00 2001
From: dilzaina99 <78306108+dilzaina99@users.noreply.github.com>
Date: Wed, 1 Oct 2025 20:38:34 +0200
Subject: [PATCH 177/232] =?UTF-8?q?Create=20Trie=20(Prefix=20Tree)=20?=
 =?UTF-8?q?=E2=80=94=20word=20&=20prefix=20lookup?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...x Tree) \342\200\224 word & prefix lookup" | 44 +++++++++++++++++++
 1 file changed, 44 insertions(+)
 create mode 100644 "C#/Searching/Trie (Prefix Tree) \342\200\224 word & prefix lookup"

diff --git "a/C#/Searching/Trie (Prefix Tree) \342\200\224 word & prefix lookup" "b/C#/Searching/Trie (Prefix Tree) \342\200\224 word & prefix lookup"
new file mode 100644
index 0000000..05379b3
--- /dev/null
+++ "b/C#/Searching/Trie (Prefix Tree) \342\200\224 word & prefix lookup"	
@@ -0,0 +1,44 @@
+using System.Collections.Generic;
+
+public class TrieNode
+{
+    public bool IsWord;
+    public Dictionary<char, TrieNode> Next = new();
+}
+
+public class Trie
+{
+    private readonly TrieNode _root = new();
+
+    public void Insert(string word)
+    {
+        var cur = _root;
+        foreach (char ch in word)
+        {
+            if (!cur.Next.ContainsKey(ch)) cur.Next[ch] = new TrieNode();
+            cur = cur.Next[ch];
+        }
+        cur.IsWord = true;
+    }
+
+    public bool Search(string word)            // exact word
+    {
+        var node = FindNode(word);
+        return node != null && node.IsWord;
+    }
+
+    public bool StartsWith(string prefix)      // prefix check
+    {
+        return FindNode(prefix) != null;
+    }
+
+    private TrieNode FindNode(string s)
+    {
+        var cur = _root;
+        foreach (char ch in s)
+        {
+            if (!cur.Next.TryGetValue(ch, out cur)) return null;
+        }
+        return cur;
+    }
+}

From 624005720f07b5ab11c585a718a1cb0624dafdd5 Mon Sep 17 00:00:00 2001
From: keerioshumaila52 <78306375+keerioshumaila52@users.noreply.github.com>
Date: Wed, 1 Oct 2025 20:45:42 +0200
Subject: [PATCH 178/232] =?UTF-8?q?Create=20Segment=20Tree=20=E2=80=94=20r?=
 =?UTF-8?q?ange=20query=20(sum)=20+=20point=20update?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...\200\224 range query (sum) + point update" | 44 +++++++++++++++++++
 1 file changed, 44 insertions(+)
 create mode 100644 "C#/Searching/Segment Tree \342\200\224 range query (sum) + point update"

diff --git "a/C#/Searching/Segment Tree \342\200\224 range query (sum) + point update" "b/C#/Searching/Segment Tree \342\200\224 range query (sum) + point update"
new file mode 100644
index 0000000..da7787c
--- /dev/null
+++ "b/C#/Searching/Segment Tree \342\200\224 range query (sum) + point update"	
@@ -0,0 +1,44 @@
+using System;
+
+public class SegmentTree
+{
+    private readonly int n;
+    private readonly long[] tree; // 1-indexed internal storage
+
+    public SegmentTree(int[] arr)
+    {
+        n = arr.Length;
+        tree = new long[4 * n];
+        Build(arr, 1, 0, n - 1);
+    }
+
+    private void Build(int[] a, int idx, int l, int r)
+    {
+        if (l == r) { tree[idx] = a[l]; return; }
+        int m = (l + r) / 2;
+        Build(a, idx * 2, l, m);
+        Build(a, idx * 2 + 1, m + 1, r);
+        tree[idx] = tree[idx * 2] + tree[idx * 2 + 1];
+    }
+
+    public long Query(int ql, int qr) => Query(1, 0, n - 1, ql, qr);
+
+    private long Query(int idx, int l, int r, int ql, int qr)
+    {
+        if (qr < l || r < ql) return 0;
+        if (ql <= l && r <= qr) return tree[idx];
+        int m = (l + r) / 2;
+        return Query(idx * 2, l, m, ql, qr) + Query(idx * 2 + 1, m + 1, r, ql, qr);
+    }
+
+    public void Update(int pos, int newValue) => Update(1, 0, n - 1, pos, newValue);
+
+    private void Update(int idx, int l, int r, int pos, int val)
+    {
+        if (l == r) { tree[idx] = val; return; }
+        int m = (l + r) / 2;
+        if (pos <= m) Update(idx * 2, l, m, pos, val);
+        else Update(idx * 2 + 1, m + 1, r, pos, val);
+        tree[idx] = tree[idx * 2] + tree[idx * 2 + 1];
+    }
+}

From 9dd68a33f07e24afb6bc5e2a6b7795b71d50fa70 Mon Sep 17 00:00:00 2001
From: keerioshumaila52 <78306375+keerioshumaila52@users.noreply.github.com>
Date: Wed, 1 Oct 2025 20:46:15 +0200
Subject: [PATCH 179/232] Create Binary Indexed Tree (Fenwick)

---
 C#/Searching/Binary Indexed Tree (Fenwick) | 52 ++++++++++++++++++++++
 1 file changed, 52 insertions(+)
 create mode 100644 C#/Searching/Binary Indexed Tree (Fenwick)

diff --git a/C#/Searching/Binary Indexed Tree (Fenwick) b/C#/Searching/Binary Indexed Tree (Fenwick)
new file mode 100644
index 0000000..db9dcf5
--- /dev/null
+++ b/C#/Searching/Binary Indexed Tree (Fenwick)	
@@ -0,0 +1,52 @@
+public class Fenwick
+{
+    private readonly long[] bit; // 1-based indexing
+    private readonly int n;
+
+    public Fenwick(int n)
+    {
+        this.n = n;
+        bit = new long[n + 1];
+    }
+
+    public Fenwick(int[] arr) : this(arr.Length)
+    {
+        for (int i = 0; i < arr.Length; i++) Add(i + 1, arr[i]);
+    }
+
+    // Add 'delta' at index i (1..n)
+    public void Add(int i, long delta)
+    {
+        for (; i <= n; i += i & -i) bit[i] += delta;
+    }
+
+    // Prefix sum [1..i]
+    public long PrefixSum(int i)
+    {
+        long s = 0;
+        for (; i > 0; i -= i & -i) s += bit[i];
+        return s;
+    }
+
+    // Range sum [l..r], with 1-based indices
+    public long RangeSum(int l, int r) => PrefixSum(r) - PrefixSum(l - 1);
+
+    // Find smallest index i such that PrefixSum(i) >= target (if monotonic, e.g., counts/frequencies)
+    public int LowerBound(long target)
+    {
+        int idx = 0;
+        int maxPow = 1;
+        while ((maxPow << 1) <= n) maxPow <<= 1;
+
+        for (int step = maxPow; step > 0; step >>= 1)
+        {
+            int next = idx + step;
+            if (next <= n && bit[next] < target)
+            {
+                idx = next;
+                target -= bit[next];
+            }
+        }
+        return idx + 1; // 1..n (caller should validate bounds)
+    }
+}

From 9b1f13f4bd44385f2c26dd38a1df3f8ec4868b47 Mon Sep 17 00:00:00 2001
From: keerioshumaila52 <78306375+keerioshumaila52@users.noreply.github.com>
Date: Wed, 1 Oct 2025 20:48:47 +0200
Subject: [PATCH 180/232] Create Graph-Based Searching

---
 C#/Searching/Graph-Based Searching | 12 ++++++++++++
 1 file changed, 12 insertions(+)
 create mode 100644 C#/Searching/Graph-Based Searching

diff --git a/C#/Searching/Graph-Based Searching b/C#/Searching/Graph-Based Searching
new file mode 100644
index 0000000..7c3d87e
--- /dev/null
+++ b/C#/Searching/Graph-Based Searching	
@@ -0,0 +1,12 @@
+using System;
+using System.Collections.Generic;
+
+// General adjacency list: for unweighted use weight=1
+public class Graph {
+    public Dictionary<int, List<(int to, double w)>> Adj = new();
+    public void AddEdge(int u, int v, double w = 1.0) {
+        if (!Adj.ContainsKey(u)) Adj[u] = new();
+        Adj[u].Add((v, w));
+        if (!Adj.ContainsKey(v)) Adj[v] = new(); // ensure key exists
+    }
+}

From 81c690eb94716f166aaaa558e0a99c58705d556b Mon Sep 17 00:00:00 2001
From: keerioshumaila52 <78306375+keerioshumaila52@users.noreply.github.com>
Date: Wed, 1 Oct 2025 20:49:05 +0200
Subject: [PATCH 181/232] Create DFS (Depth-First Search)

---
 C#/Searching/DFS (Depth-First Search) | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 C#/Searching/DFS (Depth-First Search)

diff --git a/C#/Searching/DFS (Depth-First Search) b/C#/Searching/DFS (Depth-First Search)
new file mode 100644
index 0000000..d6adb42
--- /dev/null
+++ b/C#/Searching/DFS (Depth-First Search)	
@@ -0,0 +1,13 @@
+public static class DFSAlgo {
+    public static List<int> DFS(Graph g, int start) {
+        var visited = new HashSet<int>();
+        var order = new List<int>();
+        void Dfs(int u) {
+            visited.Add(u); order.Add(u);
+            foreach (var (v, _) in g.Adj[u])
+                if (!visited.Contains(v)) Dfs(v);
+        }
+        Dfs(start);
+        return order;
+    }
+}

From c4468abace498a5b5d7c09105d6ba86aa2072e3c Mon Sep 17 00:00:00 2001
From: keerioshumaila52 <78306375+keerioshumaila52@users.noreply.github.com>
Date: Wed, 1 Oct 2025 20:49:07 +0200
Subject: [PATCH 182/232] Create Iterative Deepening DFS (IDDFS)

---
 C#/Searching/Iterative Deepening DFS (IDDFS) | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)
 create mode 100644 C#/Searching/Iterative Deepening DFS (IDDFS)

diff --git a/C#/Searching/Iterative Deepening DFS (IDDFS) b/C#/Searching/Iterative Deepening DFS (IDDFS)
new file mode 100644
index 0000000..f4b54df
--- /dev/null
+++ b/C#/Searching/Iterative Deepening DFS (IDDFS)	
@@ -0,0 +1,16 @@
+public static class IDDFS {
+    static bool DLS(Graph g, int src, int target, int depth, HashSet<int> vis) {
+        if (src == target) return true;
+        if (depth == 0) return false;
+        vis.Add(src);
+        foreach (var (v, _) in g.Adj[src])
+            if (!vis.Contains(v) && DLS(g, v, target, depth - 1, vis)) return true;
+        return false;
+    }
+
+    public static bool Search(Graph g, int start, int target, int maxDepth) {
+        for (int d = 0; d <= maxDepth; d++)
+            if (DLS(g, start, target, d, new HashSet<int>())) return true;
+        return false;
+    }
+}

From f5e200cce5bef4fce86e65a59da0e0f7e7c81914 Mon Sep 17 00:00:00 2001
From: keerioshumaila52 <78306375+keerioshumaila52@users.noreply.github.com>
Date: Wed, 1 Oct 2025 20:49:09 +0200
Subject: [PATCH 183/232] =?UTF-8?q?Create=20Uniform=20Cost=20Search=20(UCS?=
 =?UTF-8?q?)=20=E2=80=94=20same=20as=20Dijkstra=20without=20heuristic?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...00\224 same as Dijkstra without heuristic" | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)
 create mode 100644 "C#/Searching/Uniform Cost Search (UCS) \342\200\224 same as Dijkstra without heuristic"

diff --git "a/C#/Searching/Uniform Cost Search (UCS) \342\200\224 same as Dijkstra without heuristic" "b/C#/Searching/Uniform Cost Search (UCS) \342\200\224 same as Dijkstra without heuristic"
new file mode 100644
index 0000000..37a4f68
--- /dev/null
+++ "b/C#/Searching/Uniform Cost Search (UCS) \342\200\224 same as Dijkstra without heuristic"	
@@ -0,0 +1,19 @@
+public static class UCS {
+    public static Dictionary<int,double> Search(Graph g, int start) {
+        var dist = new Dictionary<int,double>();
+        foreach (var u in g.Adj.Keys) dist[u] = double.PositiveInfinity;
+        dist[start] = 0;
+        var pq = new PriorityQueue<int,double>();
+        pq.Enqueue(start, 0);
+
+        while (pq.Count > 0) {
+            pq.TryDequeue(out int u, out double d);
+            if (d != dist[u]) continue;
+            foreach (var (v, w) in g.Adj[u]) {
+                double nd = d + w;
+                if (nd < dist[v]) { dist[v] = nd; pq.Enqueue(v, nd); }
+            }
+        }
+        return dist;
+    }
+}

From 2f4f9dc8e3bda6ca8bdb840c283b2ae1ef24ffb7 Mon Sep 17 00:00:00 2001
From: git-hero1 <143338281+git-hero1@users.noreply.github.com>
Date: Wed, 1 Oct 2025 21:04:09 +0200
Subject: [PATCH 184/232] =?UTF-8?q?Create=20Dijkstra=E2=80=99s=20Algorithm?=
 =?UTF-8?q?=20(shortest=20paths,=20non-negative=20weights)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...hm (shortest paths, non-negative weights)" | 21 +++++++++++++++++++
 1 file changed, 21 insertions(+)
 create mode 100644 "Dijkstra\342\200\231s Algorithm (shortest paths, non-negative weights)"

diff --git "a/Dijkstra\342\200\231s Algorithm (shortest paths, non-negative weights)" "b/Dijkstra\342\200\231s Algorithm (shortest paths, non-negative weights)"
new file mode 100644
index 0000000..3540f71
--- /dev/null
+++ "b/Dijkstra\342\200\231s Algorithm (shortest paths, non-negative weights)"	
@@ -0,0 +1,21 @@
+public static class Dijkstra {
+    public static (Dictionary<int,double> dist, Dictionary<int,int> parent)
+    ShortestPaths(Graph g, int start) {
+        var dist = new Dictionary<int,double>();
+        var parent = new Dictionary<int,int>();
+        foreach (var u in g.Adj.Keys) dist[u] = double.PositiveInfinity;
+        dist[start] = 0;
+        var pq = new PriorityQueue<int,double>();
+        pq.Enqueue(start, 0);
+
+        while (pq.Count > 0) {
+            pq.TryDequeue(out int u, out double du);
+            if (du != dist[u]) continue;
+            foreach (var (v, w) in g.Adj[u]) {
+                double nd = du + w;
+                if (nd < dist[v]) { dist[v] = nd; parent[v] = u; pq.Enqueue(v, nd); }
+            }
+        }
+        return (dist, parent);
+    }
+}

From 29cf99bb384dde2194c43a2b73fba69e8da0c155 Mon Sep 17 00:00:00 2001
From: git-hero1 <143338281+git-hero1@users.noreply.github.com>
Date: Wed, 1 Oct 2025 21:12:22 +0200
Subject: [PATCH 185/232] =?UTF-8?q?Create=20Bidirectional=20BFS=20?=
 =?UTF-8?q?=E2=80=94=20unweighted=20shortest=20path?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...BFS \342\200\224 unweighted shortest path" | 40 +++++++++++++++++++
 1 file changed, 40 insertions(+)
 create mode 100644 "Bidirectional BFS \342\200\224 unweighted shortest path"

diff --git "a/Bidirectional BFS \342\200\224 unweighted shortest path" "b/Bidirectional BFS \342\200\224 unweighted shortest path"
new file mode 100644
index 0000000..9c3f4a4
--- /dev/null
+++ "b/Bidirectional BFS \342\200\224 unweighted shortest path"	
@@ -0,0 +1,40 @@
+public static class BidirectionalBFS {
+    public static List<int> ShortestPath(Graph g, int s, int t) {
+        if (s == t) return new() { s };
+
+        var q1 = new Queue<int>(); var q2 = new Queue<int>();
+        var p1 = new Dictionary<int,int>(); var p2 = new Dictionary<int,int>();
+        var vis1 = new HashSet<int> { s }; var vis2 = new HashSet<int> { t };
+        q1.Enqueue(s); q2.Enqueue(t);
+
+        int meet = -1;
+        while (q1.Count > 0 && q2.Count > 0) {
+            if (Step(g, q1, vis1, vis2, p1, ref meet)) break;
+            if (Step(g, q2, vis2, vis1, p2, ref meet)) break;
+        }
+        if (meet == -1) return new();
+
+        var left = new List<int>();
+        for (int x = meet; x != s; x = p1[x]) left.Add(x);
+        left.Add(s); left.Reverse();
+
+        var right = new List<int>();
+        for (int x = meet; x != t; x = p2[x]) right.Add(x);
+        right.Add(t);
+
+        left.AddRange(right.Skip(1));
+        return left;
+    }
+
+    static bool Step(Graph g, Queue<int> q, HashSet<int> own, HashSet<int> other, Dictionary<int,int> parent, ref int meet) {
+        if (q.Count == 0) return false;
+        int u = q.Dequeue();
+        foreach (var (v, _) in g.Adj[u]) {
+            if (!own.Contains(v)) {
+                own.Add(v); parent[v] = u; q.Enqueue(v);
+                if (other.Contains(v)) { meet = v; return true; }
+            }
+        }
+        return false;
+    }
+}

From 94e78938adf3e21bf3fb0f713cd68415026913dc Mon Sep 17 00:00:00 2001
From: git-hero1 <143338281+git-hero1@users.noreply.github.com>
Date: Wed, 1 Oct 2025 21:12:43 +0200
Subject: [PATCH 186/232] Create Hashing-Based Search

---
 Hashing-Based Search | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 Hashing-Based Search

diff --git a/Hashing-Based Search b/Hashing-Based Search
new file mode 100644
index 0000000..02cd7db
--- /dev/null
+++ b/Hashing-Based Search	
@@ -0,0 +1,8 @@
+var dict = new Dictionary<string, int> {
+    ["apple"] = 3, ["banana"] = 5
+};
+bool hasKey = dict.ContainsKey("apple");
+bool hasValue = dict.ContainsValue(5);
+if (dict.TryGetValue("banana", out int qty)) {
+    // qty == 5
+}

From 0707c051092427e5baf8bde5dc1199018b8df0b1 Mon Sep 17 00:00:00 2001
From: git-hero1 <143338281+git-hero1@users.noreply.github.com>
Date: Wed, 1 Oct 2025 21:12:59 +0200
Subject: [PATCH 187/232] Create Fast membership with HashSet<T>

---
 Fast membership with HashSet<T> | 2 ++
 1 file changed, 2 insertions(+)
 create mode 100644 Fast membership with HashSet<T>

diff --git a/Fast membership with HashSet<T> b/Fast membership with HashSet<T>
new file mode 100644
index 0000000..c670cad
--- /dev/null
+++ b/Fast membership with HashSet<T>	
@@ -0,0 +1,2 @@
+var set = new HashSet<int> { 1, 2, 3 };
+bool ok = set.Contains(2); // O(1) average

From 4b9e76701f5953c224e906ddb6ac0825433b6fde Mon Sep 17 00:00:00 2001
From: git-hero1 <143338281+git-hero1@users.noreply.github.com>
Date: Wed, 1 Oct 2025 21:13:13 +0200
Subject: [PATCH 188/232] Create Hill Climbing (maximize f)

---
 Hill Climbing (maximize f) | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 Hill Climbing (maximize f)

diff --git a/Hill Climbing (maximize f) b/Hill Climbing (maximize f)
new file mode 100644
index 0000000..cd196f1
--- /dev/null
+++ b/Hill Climbing (maximize f)	
@@ -0,0 +1,13 @@
+public static class HillClimb {
+    public static double Maximize(Func<double,double> f, double x0, double step=0.01, int iters=1000) {
+        double x = x0, fx = f(x);
+        for (int i=0;i<iters;i++) {
+            double xl = x - step, xr = x + step;
+            double fl = f(xl), fr = f(xr);
+            if (fl > fx && fl >= fr) { x = xl; fx = fl; }
+            else if (fr > fx)       { x = xr; fx = fr; }
+            else break; // local optimum
+        }
+        return x;
+    }
+}

From 24621c312cba2527d4b94a18da22613cc2883526 Mon Sep 17 00:00:00 2001
From: git-hero1 <143338281+git-hero1@users.noreply.github.com>
Date: Wed, 1 Oct 2025 21:13:27 +0200
Subject: [PATCH 189/232] Create Simulated Annealing (maximize f)

---
 Simulated Annealing (maximize f) | 16 ++++++++++++++++
 1 file changed, 16 insertions(+)
 create mode 100644 Simulated Annealing (maximize f)

diff --git a/Simulated Annealing (maximize f) b/Simulated Annealing (maximize f)
new file mode 100644
index 0000000..45d05ec
--- /dev/null
+++ b/Simulated Annealing (maximize f)	
@@ -0,0 +1,16 @@
+using System;
+
+public static class SimAnneal {
+    static readonly Random Rng = new();
+    public static double Maximize(Func<double,double> f, double x0, double initTemp=1.0, double cool=0.995, int iters=5000) {
+        double x = x0, fx = f(x), T = initTemp;
+        for (int i=0;i<iters;i++) {
+            double cand = x + (Rng.NextDouble()*2-1)*T; // neighbor scaled by T
+            double fc = f(cand);
+            if (fc > fx || Math.Exp((fc - fx)/T) > Rng.NextDouble()) { x = cand; fx = fc; }
+            T *= cool;
+            if (T < 1e-8) break;
+        }
+        return x;
+    }
+}

From af89b13b2a941a923e88c020960613f10e24850b Mon Sep 17 00:00:00 2001
From: fitzero1 <143338658+fitzero1@users.noreply.github.com>
Date: Wed, 1 Oct 2025 21:23:02 +0200
Subject: [PATCH 190/232] Create Genetic Algorithm (real-valued 1-D toy,
 maximize f)

---
 ...lgorithm (real-valued 1-D toy, maximize f) | 32 +++++++++++++++++++
 1 file changed, 32 insertions(+)
 create mode 100644 Genetic Algorithm (real-valued 1-D toy, maximize f)

diff --git a/Genetic Algorithm (real-valued 1-D toy, maximize f) b/Genetic Algorithm (real-valued 1-D toy, maximize f)
new file mode 100644
index 0000000..afe688c
--- /dev/null
+++ b/Genetic Algorithm (real-valued 1-D toy, maximize f)	
@@ -0,0 +1,32 @@
+using System;
+using System.Linq;
+
+public static class GA {
+    static readonly Random R = new();
+
+    public static double Maximize(Func<double,double> f, int popSize=50, int gens=200, double mutStd=0.1) {
+        double[] pop = Enumerable.Range(0, popSize).Select(_ => R.NextDouble()*2 - 1).ToArray(); // [-1,1]
+        for (int g=0; g<gens; g++) {
+            var fit = pop.Select(f).ToArray();
+            // select top half
+            var order = pop.Zip(fit).OrderByDescending(p=>p.Second).Select(p=>p.First).ToArray();
+            var elite = order.Take(popSize/2).ToArray();
+
+            // crossover + mutation to refill
+            var children = new double[popSize/2];
+            for (int i=0;i<children.Length;i++) {
+                var a = elite[R.Next(elite.Length)];
+                var b = elite[R.Next(elite.Length)];
+                double child = 0.5*(a+b) + RandNormal()*mutStd;
+                children[i] = child;
+            }
+            pop = elite.Concat(children).ToArray();
+        }
+        return pop.OrderByDescending(f).First();
+
+        double RandNormal() { // Box-Muller
+            double u1 = 1.0 - R.NextDouble(), u2 = 1.0 - R.NextDouble();
+            return Math.Sqrt(-2.0*Math.Log(u1)) * Math.Cos(2*Math.PI*u2);
+        }
+    }
+}

From 7a8a8d5a6b2d9160ebc20bf390f8e96082abb339 Mon Sep 17 00:00:00 2001
From: fitzero1 <143338658+fitzero1@users.noreply.github.com>
Date: Wed, 1 Oct 2025 21:23:11 +0200
Subject: [PATCH 191/232] Create Beam Search (keep top-k partial solutions by
 heuristic score)

---
 ...op-k partial solutions by heuristic score) | 23 +++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 Beam Search (keep top-k partial solutions by heuristic score)

diff --git a/Beam Search (keep top-k partial solutions by heuristic score) b/Beam Search (keep top-k partial solutions by heuristic score)
new file mode 100644
index 0000000..05a2f9a
--- /dev/null
+++ b/Beam Search (keep top-k partial solutions by heuristic score)	
@@ -0,0 +1,23 @@
+using System.Collections.Generic;
+using System.Linq;
+
+public static class BeamSearch {
+    public static List<string> Search(
+        string start,
+        Func<string,bool> isGoal,
+        Func<string,IEnumerable<string>> expand,
+        Func<string,double> score,
+        int beamWidth = 3,
+        int maxSteps = 50)
+    {
+        var beam = new List<string> { start };
+        for (int step=0; step<maxSteps; step++) {
+            if (beam.Any(isGoal)) return new List<string>{ beam.First(isGoal) };
+            var cand = new List<string>();
+            foreach (var s in beam) cand.AddRange(expand(s));
+            if (cand.Count == 0) break;
+            beam = cand.OrderByDescending(score).Take(beamWidth).ToList();
+        }
+        return new List<string>(); // not found
+    }
+}

From 6e02e8fc80683ef1325646460754414a605b4467 Mon Sep 17 00:00:00 2001
From: fitzero1 <143338658+fitzero1@users.noreply.github.com>
Date: Wed, 1 Oct 2025 21:23:50 +0200
Subject: [PATCH 192/232] Create Shared helpers

---
 Shared helpers | 14 ++++++++++++++
 1 file changed, 14 insertions(+)
 create mode 100644 Shared helpers

diff --git a/Shared helpers b/Shared helpers
new file mode 100644
index 0000000..e19b73e
--- /dev/null
+++ b/Shared helpers	
@@ -0,0 +1,14 @@
+public static class PathUtil
+{
+    public static List<T> ReconstructPath<T>(T start, T goal, Dictionary<T, T> parent)
+    {
+        var path = new List<T>();
+        if (!EqualityComparer<T>.Default.Equals(start, goal) && !parent.ContainsKey(goal))
+            return path; // empty: unreachable
+        for (var cur = goal; !EqualityComparer<T>.Default.Equals(cur, start); cur = parent[cur])
+            path.Add(cur);
+        path.Add(start);
+        path.Reverse();
+        return path;
+    }
+}

From 5415bbbbe967a7a6104d931ed7b01962967ee206 Mon Sep 17 00:00:00 2001
From: fitzero1 <143338658+fitzero1@users.noreply.github.com>
Date: Wed, 1 Oct 2025 21:24:12 +0200
Subject: [PATCH 193/232] Create Minimax with Alpha-Beta (generic game
 interface)

---
 ...x with Alpha-Beta (generic game interface) | 38 +++++++++++++++++++
 1 file changed, 38 insertions(+)
 create mode 100644 Minimax with Alpha-Beta (generic game interface)

diff --git a/Minimax with Alpha-Beta (generic game interface) b/Minimax with Alpha-Beta (generic game interface)
new file mode 100644
index 0000000..327ce0a
--- /dev/null
+++ b/Minimax with Alpha-Beta (generic game interface)	
@@ -0,0 +1,38 @@
+using System;
+using System.Collections.Generic;
+
+public interface IGameState<TMove> {
+    bool IsTerminal { get; }
+    double Evaluate();                 // heuristic for non-terminal cutoffs
+    IEnumerable<TMove> LegalMoves(bool maximizingPlayer);
+    IGameState<TMove> Apply(TMove move);
+}
+
+public static class AlphaBeta {
+    public static (double score, TMove best) Search<TMove>(IGameState<TMove> s, int depth, bool maxPlayer,
+        double alpha = double.NegativeInfinity, double beta = double.PositiveInfinity)
+    {
+        if (depth == 0 || s.IsTerminal) return (s.Evaluate(), default!);
+
+        TMove bestMove = default!;
+        if (maxPlayer) {
+            double best = double.NegativeInfinity;
+            foreach (var m in s.LegalMoves(true)) {
+                var (sc, _) = Search(s.Apply(m), depth-1, false, alpha, beta);
+                if (sc > best) { best = sc; bestMove = m; }
+                alpha = Math.Max(alpha, best);
+                if (beta <= alpha) break;
+            }
+            return (best, bestMove);
+        } else {
+            double best = double.PositiveInfinity;
+            foreach (var m in s.LegalMoves(false)) {
+                var (sc, _) = Search(s.Apply(m), depth-1, true, alpha, beta);
+                if (sc < best) { best = sc; bestMove = m; }
+                beta = Math.Min(beta, best);
+                if (beta <= alpha) break;
+            }
+            return (best, bestMove);
+        }
+    }
+}

From 8b90b940d27d10513357fc87ccb260a7234391e0 Mon Sep 17 00:00:00 2001
From: fitzero1 <143338658+fitzero1@users.noreply.github.com>
Date: Wed, 1 Oct 2025 21:24:38 +0200
Subject: [PATCH 194/232] =?UTF-8?q?Create=200=E2=80=931=20BFS=20(edges=20w?=
 =?UTF-8?q?eights=20=E2=88=88=20{0,1})?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...31 BFS (edges weights \342\210\210 {0,1})" | 29 +++++++++++++++++++
 1 file changed, 29 insertions(+)
 create mode 100644 "0\342\200\2231 BFS (edges weights \342\210\210 {0,1})"

diff --git "a/0\342\200\2231 BFS (edges weights \342\210\210 {0,1})" "b/0\342\200\2231 BFS (edges weights \342\210\210 {0,1})"
new file mode 100644
index 0000000..0933f29
--- /dev/null
+++ "b/0\342\200\2231 BFS (edges weights \342\210\210 {0,1})"	
@@ -0,0 +1,29 @@
+using System.Collections.Generic;
+
+public static class ZeroOneBFS
+{
+    // Returns distances from 's'. Graph: Adj[u] => list of (to, w) where w is 0 or 1
+    public static Dictionary<int,int> Dist(Dictionary<int,List<(int to, int w)>> Adj, int s)
+    {
+        var INF = int.MaxValue/4;
+        var dist = new Dictionary<int,int>();
+        foreach (var u in Adj.Keys) dist[u] = INF;
+        var dq = new LinkedList<int>();
+        dist[s] = 0; dq.AddFirst(s);
+
+        while (dq.Count > 0)
+        {
+            int u = dq.First.Value; dq.RemoveFirst();
+            foreach (var (v,w) in Adj[u])
+            {
+                int nd = dist[u] + w;
+                if (nd < dist[v])
+                {
+                    dist[v] = nd;
+                    if (w == 0) dq.AddFirst(v); else dq.AddLast(v);
+                }
+            }
+        }
+        return dist;
+    }
+}

From 1f87df51b1a8bdd0579b2f4e657ac84bfb80a131 Mon Sep 17 00:00:00 2001
From: fitzero1 <143338658+fitzero1@users.noreply.github.com>
Date: Wed, 1 Oct 2025 21:25:04 +0200
Subject: [PATCH 195/232] Create DAG Shortest Paths (topological order)

---
 DAG Shortest Paths (topological order) | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 DAG Shortest Paths (topological order)

diff --git a/DAG Shortest Paths (topological order) b/DAG Shortest Paths (topological order)
new file mode 100644
index 0000000..ece8fc3
--- /dev/null
+++ b/DAG Shortest Paths (topological order)	
@@ -0,0 +1,23 @@
+using System.Linq;
+
+public static class DAGShortestPath
+{
+    public static (Dictionary<int,double> dist, Dictionary<int,int> parent)
+    Solve(Dictionary<int,List<(int to,double w)>> Adj, IEnumerable<int> topo, int s)
+    {
+        var dist = Adj.Keys.ToDictionary(x => x, _ => double.PositiveInfinity);
+        var parent = new Dictionary<int,int>();
+        dist[s] = 0;
+
+        foreach (var u in topo)
+        {
+            if (double.IsPositiveInfinity(dist[u])) continue;
+            foreach (var (v,w) in Adj[u])
+            {
+                double nd = dist[u] + w;
+                if (nd < dist[v]) { dist[v] = nd; parent[v] = u; }
+            }
+        }
+        return (dist, parent);
+    }
+}

From 0dab9eac90072ed4006817eca8b4efb822d7722d Mon Sep 17 00:00:00 2001
From: Srinjan Ghosh <srinjanghosh11@gmail.com>
Date: Thu, 2 Oct 2025 01:24:40 +0530
Subject: [PATCH 196/232] Sorting a linked List

---
 Fast membership with HashSet<T> |  2 -
 Rust/SortLinkedList.rs          | 75 +++++++++++++++++++++++++++++++++
 2 files changed, 75 insertions(+), 2 deletions(-)
 delete mode 100644 Fast membership with HashSet<T>
 create mode 100644 Rust/SortLinkedList.rs

diff --git a/Fast membership with HashSet<T> b/Fast membership with HashSet<T>
deleted file mode 100644
index c670cad..0000000
--- a/Fast membership with HashSet<T>	
+++ /dev/null
@@ -1,2 +0,0 @@
-var set = new HashSet<int> { 1, 2, 3 };
-bool ok = set.Contains(2); // O(1) average
diff --git a/Rust/SortLinkedList.rs b/Rust/SortLinkedList.rs
new file mode 100644
index 0000000..a64377b
--- /dev/null
+++ b/Rust/SortLinkedList.rs
@@ -0,0 +1,75 @@
+use std::fmt;
+
+// A single node in the list
+struct Node {
+    value: i32,
+    next: Option<Box<Node>>,
+}
+
+// The list itself, holding the head
+pub struct LinkedList {
+    head: Option<Box<Node>>,
+}
+
+impl LinkedList {
+    /// Creates a new, empty linked list.
+    pub fn new() -> Self {
+        LinkedList { head: None }
+    }
+
+    /// Adds a new element to the front of the list.
+    pub fn push(&mut self, value: i32) {
+        let new_node = Box::new(Node {
+            value,
+            // .take() replaces self.head with None, giving us the old head
+            next: self.head.take(),
+        });
+        self.head = Some(new_node);
+    }
+
+    /// Sorts the linked list.
+    pub fn sort(&mut self) {
+        // 1. Drain the list into a Vec
+        let mut values = Vec::new();
+        let mut current = self.head.take();
+        while let Some(mut node) = current {
+            values.push(node.value);
+            current = node.next.take();
+        }
+
+        // 2. Sort the Vec
+        values.sort();
+
+        // 3. Rebuild the list from the sorted Vec
+        // We iterate in reverse to maintain the correct order when pushing to the front.
+        for value in values.iter().rev() {
+            self.push(*value);
+        }
+    }
+}
+
+// Implement the Display trait for easy printing
+impl fmt::Display for LinkedList {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        let mut current = &self.head;
+        while let Some(node) = current {
+            write!(f, "{} -> ", node.value)?;
+            current = &node.next;
+        }
+        write!(f, "None")
+    }
+}
+
+fn main() {
+    let mut list = LinkedList::new();
+    list.push(5);
+    list.push(1);
+    list.push(8);
+    list.push(3);
+
+    println!("Unsorted list: {}", list);
+
+    list.sort();
+
+    println!("Sorted list:   {}", list);
+}
\ No newline at end of file

From e6323badbf149711aaa307dcc70a4b5d610bcbce Mon Sep 17 00:00:00 2001
From: Srinjan Ghosh <srinjanghosh11@gmail.com>
Date: Thu, 2 Oct 2025 01:32:42 +0530
Subject: [PATCH 197/232] BFS Graph traversal

---
 Rust/BFSGraph.rs | 98 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 98 insertions(+)
 create mode 100644 Rust/BFSGraph.rs

diff --git a/Rust/BFSGraph.rs b/Rust/BFSGraph.rs
new file mode 100644
index 0000000..7857d90
--- /dev/null
+++ b/Rust/BFSGraph.rs
@@ -0,0 +1,98 @@
+use std::collections::VecDeque;
+
+
+/// Represents the entire graph.
+struct Graph {
+    // We store all nodes in a vector. The node's ID is its index in the vector.
+    nodes: Vec<Node>,
+}
+
+/// Represents a single node in the graph.
+struct Node {
+    // The neighbors are stored as a list of IDs (indices into the graph's nodes vector).
+    neighbors: Vec<usize>,
+}
+
+
+impl Graph {
+    /// Creates a new, empty graph.
+    fn new() -> Self {
+        Graph { nodes: Vec::new() }
+    }
+
+    /// Adds a new node to the graph and returns its ID.
+    fn add_node(&mut self) -> usize {
+        let id = self.nodes.len();
+        self.nodes.push(Node {
+            neighbors: Vec::new(),
+        });
+        id
+    }
+
+    /// Adds an undirected edge between two nodes.
+    fn add_edge(&mut self, node1_id: usize, node2_id: usize) {
+        // Ensure nodes exist before adding an edge
+        if node1_id < self.nodes.len() && node2_id < self.nodes.len() {
+            self.nodes[node1_id].neighbors.push(node2_id);
+            self.nodes[node2_id].neighbors.push(node1_id);
+        }
+    }
+}
+
+/// Performs BFS on our custom graph structure.
+fn bfs(graph: &Graph, start_node_id: usize) {
+    if start_node_id >= graph.nodes.len() {
+        println!("Error: Start node does not exist.");
+        return;
+    }
+
+    // A queue for the nodes to visit.
+    let mut queue = VecDeque::new();
+    // A boolean vector to track visited nodes. This is more efficient
+    // than a HashSet when node IDs are dense indices (0, 1, 2, ...).
+    let mut visited = vec![false; graph.nodes.len()];
+
+    // Start the search from the given node ID.
+    queue.push_back(start_node_id);
+    visited[start_node_id] = true;
+
+    println!("BFS Traversal Order:");
+
+    while let Some(current_id) = queue.pop_front() {
+        print!("{} ", current_id);
+
+        // Iterate over the neighbors of the current node.
+        for &neighbor_id in &graph.nodes[current_id].neighbors {
+            // If the neighbor hasn't been visited, mark it and add to the queue.
+            if !visited[neighbor_id] {
+                visited[neighbor_id] = true;
+                queue.push_back(neighbor_id);
+            }
+        }
+    }
+    println!(); // Add a newline at the end.
+}
+
+
+fn main() {
+    let mut graph = Graph::new();
+
+    // Create 8 nodes, with IDs from 0 to 7.
+    for _ in 0..8 {
+        graph.add_node();
+    }
+
+    // Add edges to create the same graph structure as before.
+    graph.add_edge(0, 1);
+    graph.add_edge(0, 2);
+    graph.add_edge(1, 3);
+    graph.add_edge(1, 4);
+    graph.add_edge(2, 4);
+    graph.add_edge(3, 5);
+    graph.add_edge(4, 5);
+    graph.add_edge(6, 7); // A disconnected component
+
+    let start_node = 0;
+    println!("Starting BFS from node {}", start_node);
+    bfs(&graph, start_node);
+}
\ No newline at end of file

From 23ae8b5a7e9701450ec2afff01e2c6c6c42c8d6b Mon Sep 17 00:00:00 2001
From: Srinjan Ghosh <srinjanghosh11@gmail.com>
Date: Thu, 2 Oct 2025 01:36:42 +0530
Subject: [PATCH 198/232] DFS Graph

---
 Rust/DFSGraph.rs | 88 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 88 insertions(+)
 create mode 100644 Rust/DFSGraph.rs

diff --git a/Rust/DFSGraph.rs b/Rust/DFSGraph.rs
new file mode 100644
index 0000000..633fc10
--- /dev/null
+++ b/Rust/DFSGraph.rs
@@ -0,0 +1,88 @@
+/// Represents the entire graph.
+struct Graph {
+    // We store all nodes in a vector. The node's ID is its index in the vector.
+    nodes: Vec<Node>,
+}
+
+/// Represents a single node in the graph.
+struct Node {
+    // The neighbors are stored as a list of IDs (indices into the graph's nodes vector).
+    neighbors: Vec<usize>,
+}
+
+impl Graph {
+    /// Creates a new, empty graph.
+    fn new() -> Self {
+        Graph { nodes: Vec::new() }
+    }
+
+    /// Adds a new node to the graph and returns its ID.
+    fn add_node(&mut self) -> usize {
+        let id = self.nodes.len();
+        self.nodes.push(Node {
+            neighbors: Vec::new(),
+        });
+        id
+    }
+
+    /// Adds an undirected edge between two nodes.
+    fn add_edge(&mut self, node1_id: usize, node2_id: usize) {
+        if node1_id < self.nodes.len() && node2_id < self.nodes.len() {
+            self.nodes[node1_id].neighbors.push(node2_id);
+            self.nodes[node2_id].neighbors.push(node1_id);
+        }
+    }
+}
+
+/// A recursive helper function for the DFS traversal.
+fn dfs_recursive(graph: &Graph, current_id: usize, visited: &mut Vec<bool>) {
+    // Mark the current node as visited and process it.
+    visited[current_id] = true;
+    print!("{} ", current_id);
+
+    // Recur for all adjacent vertices that haven't been visited yet.
+    for &neighbor_id in &graph.nodes[current_id].neighbors {
+        if !visited[neighbor_id] {
+            dfs_recursive(graph, neighbor_id, visited);
+        }
+    }
+}
+
+/// The main DFS function that initializes the search.
+fn dfs(graph: &Graph, start_node_id: usize) {
+    if start_node_id >= graph.nodes.len() {
+        println!("Error: Start node does not exist.");
+        return;
+    }
+
+    // A boolean vector to track visited nodes.
+    let mut visited = vec![false; graph.nodes.len()];
+    
+    println!("DFS Traversal Order:");
+    dfs_recursive(graph, start_node_id, &mut visited);
+    println!(); // Add a newline at the end.
+}
+
+
+fn main() {
+    let mut graph = Graph::new();
+
+    // Create 8 nodes, with IDs from 0 to 7.
+    for _ in 0..8 {
+        graph.add_node();
+    }
+
+    // Add edges to create the same graph structure.
+    graph.add_edge(0, 1);
+    graph.add_edge(0, 2);
+    graph.add_edge(1, 3);
+    graph.add_edge(1, 4);
+    graph.add_edge(2, 4);
+    graph.add_edge(3, 5);
+    graph.add_edge(4, 5);
+    graph.add_edge(6, 7); // A disconnected component
+
+    let start_node = 0;
+    println!("Starting DFS from node {}", start_node);
+    dfs(&graph, start_node);
+}
\ No newline at end of file

From 6e5babdcda89a1c83df83be2170122b750619f33 Mon Sep 17 00:00:00 2001
From: Srinjan Ghosh <46373606+srinjan-ghosh@users.noreply.github.com>
Date: Thu, 2 Oct 2025 01:46:41 +0530
Subject: [PATCH 199/232] Create DFSGraph.rs

---
 Rust/DFSGraph.rs | 88 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 88 insertions(+)
 create mode 100644 Rust/DFSGraph.rs

diff --git a/Rust/DFSGraph.rs b/Rust/DFSGraph.rs
new file mode 100644
index 0000000..9c6c1a5
--- /dev/null
+++ b/Rust/DFSGraph.rs
@@ -0,0 +1,88 @@
+/// Represents the entire graph.
+struct Graph {
+    // We store all nodes in a vector. The node's ID is its index in the vector.
+    nodes: Vec<Node>,
+}
+
+/// Represents a single node in the graph.
+struct Node {
+    // The neighbors are stored as a list of IDs (indices into the graph's nodes vector).
+    neighbors: Vec<usize>,
+}
+
+impl Graph {
+    /// Creates a new, empty graph.
+    fn new() -> Self {
+        Graph { nodes: Vec::new() }
+    }
+
+    /// Adds a new node to the graph and returns its ID.
+    fn add_node(&mut self) -> usize {
+        let id = self.nodes.len();
+        self.nodes.push(Node {
+            neighbors: Vec::new(),
+        });
+        id
+    }
+
+    /// Adds an undirected edge between two nodes.
+    fn add_edge(&mut self, node1_id: usize, node2_id: usize) {
+        if node1_id < self.nodes.len() && node2_id < self.nodes.len() {
+            self.nodes[node1_id].neighbors.push(node2_id);
+            self.nodes[node2_id].neighbors.push(node1_id);
+        }
+    }
+}
+
+/// A recursive helper function for the DFS traversal.
+fn dfs_recursive(graph: &Graph, current_id: usize, visited: &mut Vec<bool>) {
+    // Mark the current node as visited and process it.
+    visited[current_id] = true;
+    print!("{} ", current_id);
+
+    // Recur for all adjacent vertices that haven't been visited yet.
+    for &neighbor_id in &graph.nodes[current_id].neighbors {
+        if !visited[neighbor_id] {
+            dfs_recursive(graph, neighbor_id, visited);
+        }
+    }
+}
+
+/// The main DFS function that initializes the search.
+fn dfs(graph: &Graph, start_node_id: usize) {
+    if start_node_id >= graph.nodes.len() {
+        println!("Error: Start node does not exist.");
+        return;
+    }
+
+    // A boolean vector to track visited nodes.
+    let mut visited = vec![false; graph.nodes.len()];
+    
+    println!("DFS Traversal Order:");
+    dfs_recursive(graph, start_node_id, &mut visited);
+    println!(); // Add a newline at the end.
+}
+
+
+fn main() {
+    let mut graph = Graph::new();
+
+    // Create 8 nodes, with IDs from 0 to 7.
+    for _ in 0..8 {
+        graph.add_node();
+    }
+
+    // Add edges to create the same graph structure.
+    graph.add_edge(0, 1);
+    graph.add_edge(0, 2);
+    graph.add_edge(1, 3);
+    graph.add_edge(1, 4);
+    graph.add_edge(2, 4);
+    graph.add_edge(3, 5);
+    graph.add_edge(4, 5);
+    graph.add_edge(6, 7); // A disconnected component
+
+    let start_node = 0;
+    println!("Starting DFS from node {}", start_node);
+    dfs(&graph, start_node);
+}

From 17da05077d0705f2b36215e9746d2fb625f4cb3c Mon Sep 17 00:00:00 2001
From: Srinjan Ghosh <46373606+srinjan-ghosh@users.noreply.github.com>
Date: Thu, 2 Oct 2025 01:50:22 +0530
Subject: [PATCH 200/232] InorderTraversal.rs

---
 Rust/InorderTraversal.rs | 73 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 73 insertions(+)
 create mode 100644 Rust/InorderTraversal.rs

diff --git a/Rust/InorderTraversal.rs b/Rust/InorderTraversal.rs
new file mode 100644
index 0000000..fd467d2
--- /dev/null
+++ b/Rust/InorderTraversal.rs
@@ -0,0 +1,73 @@
+// A node in the binary tree.
+struct Node {
+    value: i32,
+    // We use Option<Box<Node>> to allow for nullable, heap-allocated children.
+    // Box<T> allows us to have a recursive type with a known size.
+    left: Option<Box<Node>>,
+    right: Option<Box<Node>>,
+}
+
+// A simple representation of the tree itself.
+struct BinaryTree {
+    root: Option<Box<Node>>,
+}
+
+
+/// A recursive helper function to perform the in-order traversal.
+fn in_order_recursive(node: &Option<Box<Node>>, result: &mut Vec<i32>) {
+    // Check if the current node exists.
+    if let Some(current_node) = node {
+        // 1. Traverse the left subtree.
+        in_order_recursive(&current_node.left, result);
+
+        // 2. Visit the root node.
+        result.push(current_node.value);
+
+        // 3. Traverse the right subtree.
+        in_order_recursive(&current_node.right, result);
+    }
+}
+
+/// The public function to start the in-order traversal.
+pub fn in_order_traversal(tree: &BinaryTree) -> Vec<i32> {
+    let mut result = Vec::new();
+    in_order_recursive(&tree.root, &mut result);
+    result
+}
+
+
+fn main() {
+    // Manually construct a sample binary tree.
+    //      4
+    //     / \
+    //    2   5
+    //   / \
+    //  1   3
+    let tree = BinaryTree {
+        root: Some(Box::new(Node {
+            value: 4,
+            left: Some(Box::new(Node {
+                value: 2,
+                left: Some(Box::new(Node {
+                    value: 1,
+                    left: None,
+                    right: None,
+                })),
+                right: Some(Box::new(Node {
+                    value: 3,
+                    left: None,
+                    right: None,
+                })),
+            })),
+            right: Some(Box::new(Node {
+                value: 5,
+                left: None,
+                right: None,
+            })),
+        })),
+    };
+
+    let traversal_result = in_order_traversal(&tree);
+
+    println!("In-order traversal result: {:?}", traversal_result);
+}

From e7da05e51195c53480ca18a25885d263a87161d9 Mon Sep 17 00:00:00 2001
From: Srinjan Ghosh <46373606+srinjan-ghosh@users.noreply.github.com>
Date: Thu, 2 Oct 2025 01:52:33 +0530
Subject: [PATCH 201/232] Create PreOrderTraversal.ts

---
 Rust/PreOrderTraversal.ts | 76 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100644 Rust/PreOrderTraversal.ts

diff --git a/Rust/PreOrderTraversal.ts b/Rust/PreOrderTraversal.ts
new file mode 100644
index 0000000..e4efe38
--- /dev/null
+++ b/Rust/PreOrderTraversal.ts
@@ -0,0 +1,76 @@
+// ## The Data Structures ##
+
+// A node in the binary tree.
+struct Node {
+    value: i32,
+    // Option<Box<Node>> allows for nullable, heap-allocated children.
+    left: Option<Box<Node>>,
+    right: Option<Box<Node>>,
+}
+
+// Represents the tree itself.
+struct BinaryTree {
+    root: Option<Box<Node>>,
+}
+
+// ## Implementation ##
+
+/// A recursive helper function for the pre-order traversal.
+fn pre_order_recursive(node: &Option<Box<Node>>, result: &mut Vec<i32>) {
+    // Check if the current node exists.
+    if let Some(current_node) = node {
+        // 1. Visit the root node first.
+        result.push(current_node.value);
+
+        // 2. Traverse the left subtree.
+        pre_order_recursive(&current_node.left, result);
+
+        // 3. Traverse the right subtree.
+        pre_order_recursive(&current_node.right, result);
+    }
+}
+
+/// The public function to start the pre-order traversal.
+pub fn pre_order_traversal(tree: &BinaryTree) -> Vec<i32> {
+    let mut result = Vec::new();
+    pre_order_recursive(&tree.root, &mut result);
+    result
+}
+
+// ## Putting It All Together ##
+
+fn main() {
+    // Manually construct a sample binary tree.
+    //      4
+    //     / \
+    //    2   5
+    //   / \
+    //  1   3
+    let tree = BinaryTree {
+        root: Some(Box::new(Node {
+            value: 4,
+            left: Some(Box::new(Node {
+                value: 2,
+                left: Some(Box::new(Node {
+                    value: 1,
+                    left: None,
+                    right: None,
+                })),
+                right: Some(Box::new(Node {
+                    value: 3,
+                    left: None,
+                    right: None,
+                })),
+            })),
+            right: Some(Box::new(Node {
+                value: 5,
+                left: None,
+                right: None,
+            })),
+        })),
+    };
+
+    let traversal_result = pre_order_traversal(&tree);
+
+    println!("Pre-order traversal result: {:?}", traversal_result);
+}

From 4b3cb7489987d140971976c73c3a21917ca966ef Mon Sep 17 00:00:00 2001
From: Srinjan Ghosh <46373606+srinjan-ghosh@users.noreply.github.com>
Date: Thu, 2 Oct 2025 01:54:26 +0530
Subject: [PATCH 202/232] Create PostOrderTraversal.rs

---
 Rust/PostOrderTraversal.rs | 76 ++++++++++++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100644 Rust/PostOrderTraversal.rs

diff --git a/Rust/PostOrderTraversal.rs b/Rust/PostOrderTraversal.rs
new file mode 100644
index 0000000..afa3585
--- /dev/null
+++ b/Rust/PostOrderTraversal.rs
@@ -0,0 +1,76 @@
+// ## The Data Structures ##
+
+// A node in the binary tree.
+struct Node {
+    value: i32,
+    // Option<Box<Node>> allows for nullable, heap-allocated children.
+    left: Option<Box<Node>>,
+    right: Option<Box<Node>>,
+}
+
+// Represents the tree itself.
+struct BinaryTree {
+    root: Option<Box<Node>>,
+}
+
+// ## Implementation ##
+
+/// A recursive helper function for the post-order traversal.
+fn post_order_recursive(node: &Option<Box<Node>>, result: &mut Vec<i32>) {
+    // Check if the current node exists.
+    if let Some(current_node) = node {
+        // 1. Traverse the left subtree.
+        post_order_recursive(&current_node.left, result);
+
+        // 2. Traverse the right subtree.
+        post_order_recursive(&current_node.right, result);
+
+        // 3. Visit the root node last.
+        result.push(current_node.value);
+    }
+}
+
+/// The public function to start the post-order traversal.
+pub fn post_order_traversal(tree: &BinaryTree) -> Vec<i32> {
+    let mut result = Vec::new();
+    post_order_recursive(&tree.root, &mut result);
+    result
+}
+
+// ## Putting It All Together ##
+
+fn main() {
+    // Manually construct a sample binary tree.
+    //      4
+    //     / \
+    //    2   5
+    //   / \
+    //  1   3
+    let tree = BinaryTree {
+        root: Some(Box::new(Node {
+            value: 4,
+            left: Some(Box::new(Node {
+                value: 2,
+                left: Some(Box::new(Node {
+                    value: 1,
+                    left: None,
+                    right: None,
+                })),
+                right: Some(Box::new(Node {
+                    value: 3,
+                    left: None,
+                    right: None,
+                })),
+            })),
+            right: Some(Box::new(Node {
+                value: 5,
+                left: None,
+                right: None,
+            })),
+        })),
+    };
+
+    let traversal_result = post_order_traversal(&tree);
+
+    println!("Post-order traversal result: {:?}", traversal_result);
+}

From f48f48eade63bbd51ffc5f80690cea6c914fd16a Mon Sep 17 00:00:00 2001
From: angryusergit1 <143338893+angryusergit1@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:27:07 +0200
Subject: [PATCH 203/232] Create Bidirectional Dijkstra (faster on sparse,
 weighted graphs)

---
 ...jkstra (faster on sparse, weighted graphs) | 79 +++++++++++++++++++
 1 file changed, 79 insertions(+)
 create mode 100644 C#/Searching/Bidirectional Dijkstra (faster on sparse, weighted graphs)

diff --git a/C#/Searching/Bidirectional Dijkstra (faster on sparse, weighted graphs) b/C#/Searching/Bidirectional Dijkstra (faster on sparse, weighted graphs)
new file mode 100644
index 0000000..a956749
--- /dev/null
+++ b/C#/Searching/Bidirectional Dijkstra (faster on sparse, weighted graphs)	
@@ -0,0 +1,79 @@
+using System;
+using System.Collections.Generic;
+
+public static class BiDijkstra
+{
+    public static List<int> ShortestPath(
+        Dictionary<int,List<(int to,double w)>> Adj, int s, int t)
+    {
+        var INF = double.PositiveInfinity;
+        var distF = new Dictionary<int,double>(); var distB = new Dictionary<int,double>();
+        var prevF = new Dictionary<int,int>();   var prevB = new Dictionary<int,int>();
+        foreach (var u in Adj.Keys) { distF[u] = INF; distB[u] = INF; }
+        distF[s] = 0; distB[t] = 0;
+
+        var pqF = new PriorityQueue<int,double>(); var pqB = new PriorityQueue<int,double>();
+        pqF.Enqueue(s, 0); pqB.Enqueue(t, 0);
+        var visitedF = new HashSet<int>(); var visitedB = new HashSet<int>();
+        int meet = -1; double best = INF;
+
+        while (pqF.Count > 0 && pqB.Count > 0)
+        {
+            // forward step
+            pqF.TryDequeue(out int u, out double du);
+            if (visitedF.Add(u))
+            {
+                if (distF[u] + distB.GetValueOrDefault(u, INF) < best) {
+                    best = distF[u] + distB[u]; meet = u;
+                }
+                foreach (var (v,w) in Adj[u])
+                {
+                    double nd = du + w;
+                    if (nd < distF[v]) { distF[v] = nd; prevF[v] = u; pqF.Enqueue(v, nd); }
+                }
+            }
+
+            // backward step (using reverse edges)
+            pqB.TryDequeue(out int x, out double dx);
+            if (visitedB.Add(x))
+            {
+                if (distF.GetValueOrDefault(x, INF) + distB[x] < best) {
+                    best = distF[x] + distB[x]; meet = x;
+                }
+                foreach (var (p,w) in InNeighbors(Adj, x)) // build or cache reverse adjacency
+                {
+                    double nd = dx + w;
+                    if (nd < distB[p]) { distB[p] = nd; prevB[p] = x; pqB.Enqueue(p, nd); }
+                }
+            }
+
+            if (meet != -1 && pqF.Count > 0 && pqB.Count > 0 &&
+                pqF.PeekPriority() + pqB.PeekPriority() >= best) break;
+        }
+        if (meet == -1) return new();
+
+        var left = PathUtil.ReconstructPath(s, meet, prevF);
+        var right = new List<int>();
+        for (int cur = meet; cur != t; cur = prevB[cur]) right.Add(cur);
+        right.Add(t);
+        left.AddRange(right.Skip(1));
+        return left;
+    }
+
+    // Helper to get reverse neighbors on the fly
+    static IEnumerable<(int from,double w)> InNeighbors(
+        Dictionary<int,List<(int to,double w)>> Adj, int v)
+    {
+        foreach (var (u, edges) in Adj)
+            foreach (var (to,w) in edges)
+                if (to == v) yield return (u,w);
+    }
+
+    // PeekPriority extension for PriorityQueue
+    static double PeekPriority(this PriorityQueue<int,double> pq)
+    {
+        pq.TryDequeue(out int u, out double p);
+        pq.Enqueue(u, p);
+        return p;
+    }
+}

From ba70a71b939f4afee7c485af0e96cc149971b61d Mon Sep 17 00:00:00 2001
From: angryusergit1 <143338893+angryusergit1@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:28:28 +0200
Subject: [PATCH 204/232] =?UTF-8?q?Create=20IDA*=20(Iterative=20Deepening?=
 =?UTF-8?q?=20A*)=20=E2=80=94=20memory-light=20optimal=20search?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ... \342\200\224 memory-light optimal search" | 40 +++++++++++++++++++
 1 file changed, 40 insertions(+)
 create mode 100644 "C#/Searching/IDA* (Iterative Deepening A*) \342\200\224 memory-light optimal search"

diff --git "a/C#/Searching/IDA* (Iterative Deepening A*) \342\200\224 memory-light optimal search" "b/C#/Searching/IDA* (Iterative Deepening A*) \342\200\224 memory-light optimal search"
new file mode 100644
index 0000000..8d4bea7
--- /dev/null
+++ "b/C#/Searching/IDA* (Iterative Deepening A*) \342\200\224 memory-light optimal search"	
@@ -0,0 +1,40 @@
+using System;
+using System.Collections.Generic;
+
+public static class IDAStar
+{
+    public static List<int> FindPath(
+        Dictionary<int,List<(int to,double w)>> Adj, int start, int goal, Func<int,double> h)
+    {
+        double bound = h(start);
+        var path = new List<int> { start };
+        while (true)
+        {
+            double t = Search(path, 0, bound, goal, h, Adj);
+            if (t == -1) return new List<int>(path);
+            if (double.IsPositiveInfinity(t)) return new();
+            bound = t;
+        }
+    }
+
+    static double Search(List<int> path, double g, double bound, int goal,
+                         Func<int,double> h, Dictionary<int,List<(int to,double w)>> Adj)
+    {
+        int node = path[^1];
+        double f = g + h(node);
+        if (f > bound) return f;
+        if (node == goal) return -1;
+
+        double min = double.PositiveInfinity;
+        foreach (var (v,w) in Adj[node])
+        {
+            if (path.Contains(v)) continue; // avoid cycles
+            path.Add(v);
+            double t = Search(path, g + w, bound, goal, h, Adj);
+            if (t == -1) return -1;
+            if (t < min) min = t;
+            path.RemoveAt(path.Count - 1);
+        }
+        return min;
+    }
+}

From ac10ef52e1f2de7266de082ce723c9581d7f609d Mon Sep 17 00:00:00 2001
From: angryusergit1 <143338893+angryusergit1@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:28:29 +0200
Subject: [PATCH 205/232] Create Custom key with IEqualityComparer<T>

---
 C#/Searching/Custom key with IEqualityComparer<T> | 15 +++++++++++++++
 1 file changed, 15 insertions(+)
 create mode 100644 C#/Searching/Custom key with IEqualityComparer<T>

diff --git a/C#/Searching/Custom key with IEqualityComparer<T> b/C#/Searching/Custom key with IEqualityComparer<T>
new file mode 100644
index 0000000..38c3eda
--- /dev/null
+++ b/C#/Searching/Custom key with IEqualityComparer<T>	
@@ -0,0 +1,15 @@
+using System;
+using System.Collections.Generic;
+
+public readonly record struct Point(int X, int Y);
+
+public sealed class PointComparer : IEqualityComparer<Point>
+{
+    public bool Equals(Point a, Point b) => a.X == b.X && a.Y == b.Y;
+    public int GetHashCode(Point p) => HashCode.Combine(p.X, p.Y);
+}
+
+// Usage:
+var dict = new Dictionary<Point, string>(new PointComparer());
+dict[new Point(1,2)] = "A";
+bool ok = dict.ContainsKey(new Point(1,2));

From 9767821d93e0c87972087f903defcbcd019791cd Mon Sep 17 00:00:00 2001
From: angryusergit1 <143338893+angryusergit1@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:28:30 +0200
Subject: [PATCH 206/232] Create ConcurrentDictionary for thread-safe lookups

---
 C#/Searching/ConcurrentDictionary for thread-safe lookups | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 C#/Searching/ConcurrentDictionary for thread-safe lookups

diff --git a/C#/Searching/ConcurrentDictionary for thread-safe lookups b/C#/Searching/ConcurrentDictionary for thread-safe lookups
new file mode 100644
index 0000000..0b1bb0b
--- /dev/null
+++ b/C#/Searching/ConcurrentDictionary for thread-safe lookups	
@@ -0,0 +1,5 @@
+using System.Collections.Concurrent;
+
+var cache = new ConcurrentDictionary<string,int>();
+int value = cache.GetOrAdd("key", _ => ExpensiveCompute());
+bool exists = cache.ContainsKey("key");

From 47c4e22efe4f6280991dadbb325f5757be496ae9 Mon Sep 17 00:00:00 2001
From: angryusergit1 <143338893+angryusergit1@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:28:31 +0200
Subject: [PATCH 207/232] Create HashSet<T> with custom comparer (fast
 membership)

---
 C#/Searching/HashSet<T> with custom comparer (fast membership) | 2 ++
 1 file changed, 2 insertions(+)
 create mode 100644 C#/Searching/HashSet<T> with custom comparer (fast membership)

diff --git a/C#/Searching/HashSet<T> with custom comparer (fast membership) b/C#/Searching/HashSet<T> with custom comparer (fast membership)
new file mode 100644
index 0000000..7fa5df9
--- /dev/null
+++ b/C#/Searching/HashSet<T> with custom comparer (fast membership)	
@@ -0,0 +1,2 @@
+var set = new HashSet<Point>(new PointComparer()) { new(0,0), new(1,2) };
+bool seen = set.Contains(new Point(1,2));

From 774cb73da0581f3d344b48b90793e14a527f65fe Mon Sep 17 00:00:00 2001
From: angryusergit1 <143338893+angryusergit1@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:28:45 +0200
Subject: [PATCH 208/232] Create RBFS (Recursive Best-First Search)

---
 .../RBFS (Recursive Best-First Search)        | 49 +++++++++++++++++++
 1 file changed, 49 insertions(+)
 create mode 100644 C#/Searching/RBFS (Recursive Best-First Search)

diff --git a/C#/Searching/RBFS (Recursive Best-First Search) b/C#/Searching/RBFS (Recursive Best-First Search)
new file mode 100644
index 0000000..5067014
--- /dev/null
+++ b/C#/Searching/RBFS (Recursive Best-First Search)	
@@ -0,0 +1,49 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+
+public static class RBFS
+{
+    public static List<int> Search(
+        Dictionary<int,List<(int to,double w)>> Adj, int start, int goal, Func<int,double> h)
+    {
+        var path = new List<int> { start };
+        double fLimit = h(start);
+        while (true)
+        {
+            var (result, newLimit) = RBFSInner(path, 0, fLimit, goal, h, Adj);
+            if (result != null) return result;
+            if (double.IsPositiveInfinity(newLimit)) return new();
+            fLimit = newLimit;
+        }
+    }
+
+    static (List<int> result, double fLimit) RBFSInner(
+        List<int> path, double g, double fLimit, int goal,
+        Func<int,double> h, Dictionary<int,List<(int to,double w)>> Adj)
+    {
+        int node = path[^1];
+        if (node == goal) return (new List<int>(path), fLimit);
+
+        var succ = new List<(int v, double g, double f)>();
+        foreach (var (v,w) in Adj[node])
+            if (!path.Contains(v))
+                succ.Add((v, g + w, Math.Max(g + w + h(v), fLimit))); // classic RBFS uses parent f as floor
+
+        if (succ.Count == 0) return (null, double.PositiveInfinity);
+
+        while (true)
+        {
+            succ = succ.OrderBy(s => s.f).ToList();
+            var best = succ[0];
+            if (best.f > fLimit) return (null, best.f);
+
+            double alt = succ.Count > 1 ? succ[1].f : double.PositiveInfinity;
+            path.Add(best.v);
+            var (res, newF) = RBFSInner(path, best.g, Math.Min(fLimit, alt), goal, h, Adj);
+            path.RemoveAt(path.Count - 1);
+            succ[0] = (best.v, best.g, newF);
+            if (res != null) return (res, newF);
+        }
+    }
+}

From 76510616185a226ab0da4bac5c211b1cf8c80524 Mon Sep 17 00:00:00 2001
From: coolusergit1 <143963070+coolusergit1@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:39:12 +0200
Subject: [PATCH 209/232] =?UTF-8?q?Create=20MCTS=20(Monte=20Carlo=20Tree?=
 =?UTF-8?q?=20Search)=20=E2=80=94=20tiny,=20generic=20skeleton?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...arch) \342\200\224 tiny, generic skeleton" | 84 +++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 "C#/Searching/MCTS (Monte Carlo Tree Search) \342\200\224 tiny, generic skeleton"

diff --git "a/C#/Searching/MCTS (Monte Carlo Tree Search) \342\200\224 tiny, generic skeleton" "b/C#/Searching/MCTS (Monte Carlo Tree Search) \342\200\224 tiny, generic skeleton"
new file mode 100644
index 0000000..04a4c35
--- /dev/null
+++ "b/C#/Searching/MCTS (Monte Carlo Tree Search) \342\200\224 tiny, generic skeleton"	
@@ -0,0 +1,84 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+
+public interface IMctsState<TMove>
+{
+    bool IsTerminal { get; }
+    int CurrentPlayer { get; }                 // +1 / -1 or 0/1
+    IEnumerable<TMove> LegalMoves();
+    IMctsState<TMove> Apply(TMove move);
+    double RolloutValue();                     // random playout result from this state (from current player's pov)
+}
+
+public class MCTS<TMove>
+{
+    class Node {
+        public IMctsState<TMove> S;
+        public Node Parent;
+        public TMove Move;
+        public List<Node> Children = new();
+        public int N;          // visits
+        public double W;       // total reward
+        public bool FullyExpanded => Children.Count == _moves.Count;
+        List<TMove> _moves;
+        public Node(IMctsState<TMove> s, Node p = null, TMove m = default)
+        { S = s; Parent = p; Move = m; _moves = s.LegalMoves().ToList(); }
+        public IEnumerable<TMove> UntriedMoves() =>
+            _moves.Where(m => !Children.Any(c => EqualityComparer<TMove>.Default.Equals(c.Move, m)));
+    }
+
+    readonly Random _rng = new();
+    readonly int _iterations;
+    readonly double _c;
+
+    public MCTS(int iterations = 5000, double explorationC = 1.414) { _iterations = iterations; _c = explorationC; }
+
+    public TMove Search(IMctsState<TMove> root)
+    {
+        var R = new Node(root);
+        for (int i=0; i<_iterations; i++)
+        {
+            var node = Select(R);
+            if (!node.S.IsTerminal && node.UntriedMoves().Any())
+                node = Expand(node);
+            double result = Simulate(node.S);
+            Backprop(node, result, node.S.CurrentPlayer);
+        }
+        return R.Children.OrderByDescending(c => c.N).First().Move;
+    }
+
+    Node Select(Node n)
+    {
+        while (!n.S.IsTerminal && n.Children.Count > 0 && n.FullyExpanded)
+            n = n.Children.OrderByDescending(UCT).First();
+        return n;
+
+        double UCT(Node x)
+        {
+            if (x.N == 0) return double.PositiveInfinity;
+            return x.W / x.N + _c * Math.Sqrt(Math.Log(n.N + 1) / x.N);
+        }
+    }
+
+    Node Expand(Node n)
+    {
+        var m = n.UntriedMoves().OrderBy(_ => _rng.Next()).First();
+        var child = new Node(n.S.Apply(m), n, m);
+        n.Children.Add(child);
+        return child;
+    }
+
+    double Simulate(IMctsState<TMove> s) => s.RolloutValue();
+
+    void Backprop(Node n, double value, int playerAtNode)
+    {
+        while (n != null)
+        {
+            n.N++;
+            // assume value is from perspective of player that started this state's turn.
+            n.W += value;
+            n = n.Parent;
+        }
+    }
+}

From f4d5b476c1b1ac3ec3ce734b9b894fb65dd22fa4 Mon Sep 17 00:00:00 2001
From: coolusergit1 <143963070+coolusergit1@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:39:13 +0200
Subject: [PATCH 210/232] Create Tabu Search (outline for combinatorial
 optimization)

---
 ...h (outline for combinatorial optimization) | 37 +++++++++++++++++++
 1 file changed, 37 insertions(+)
 create mode 100644 C#/Searching/Tabu Search (outline for combinatorial optimization)

diff --git a/C#/Searching/Tabu Search (outline for combinatorial optimization) b/C#/Searching/Tabu Search (outline for combinatorial optimization)
new file mode 100644
index 0000000..fc31441
--- /dev/null
+++ b/C#/Searching/Tabu Search (outline for combinatorial optimization)	
@@ -0,0 +1,37 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+
+public static class TabuSearch
+{
+    public static T Optimize<T>(
+        T start,
+        Func<T,double> score,
+        Func<T,IEnumerable<T>> neighbors,
+        int tabuSize = 50,
+        int maxIter = 2000)
+    {
+        var best = start; double bestVal = score(best);
+        var cur = start;  double curVal  = bestVal;
+        var tabu = new Queue<T>();
+        var tabuSet = new HashSet<T>();
+
+        void AddTabu(T x) { tabu.Enqueue(x); tabuSet.Add(x); if (tabu.Count > tabuSize) tabuSet.Remove(tabu.Dequeue()); }
+
+        for (int it=0; it<maxIter; it++)
+        {
+            var cand = neighbors(cur)
+                .Where(n => !tabuSet.Contains(n))
+                .Select(n => (n, v: score(n)))
+                .OrderByDescending(p => p.v)
+                .FirstOrDefault();
+
+            if (EqualityComparer<T>.Default.Equals(cand.n, default)) break;
+
+            cur = cand.n; curVal = cand.v;
+            AddTabu(cur);
+            if (curVal > bestVal) { best = cur; bestVal = curVal; }
+        }
+        return best;
+    }
+}

From 2db22fa806a2b3bba45f90b9b52c636e2b2e4d66 Mon Sep 17 00:00:00 2001
From: coolusergit1 <143963070+coolusergit1@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:39:15 +0200
Subject: [PATCH 211/232] Create Iterated Local Search (simple meta-heuristic)

---
 ...rated Local Search (simple meta-heuristic) | 25 +++++++++++++++++++
 1 file changed, 25 insertions(+)
 create mode 100644 C#/Searching/Iterated Local Search (simple meta-heuristic)

diff --git a/C#/Searching/Iterated Local Search (simple meta-heuristic) b/C#/Searching/Iterated Local Search (simple meta-heuristic)
new file mode 100644
index 0000000..d3df683
--- /dev/null
+++ b/C#/Searching/Iterated Local Search (simple meta-heuristic)	
@@ -0,0 +1,25 @@
+using System;
+
+public static class IteratedLocalSearch
+{
+    static readonly Random R = new();
+
+    public static T Maximize<T>(
+        T start,
+        Func<T,double> score,
+        Func<T,T> localImprove,
+        Func<T,T> perturb,
+        int iters = 200)
+    {
+        var best = localImprove(start);
+        double bestVal = score(best);
+
+        for (int i=0; i<iters; i++)
+        {
+            var cand = localImprove(perturb(best));
+            double val = score(cand);
+            if (val > bestVal) { best = cand; bestVal = val; }
+        }
+        return best;
+    }
+}

From 0976bd99b22b8c3eb053509c85c854e9a9e5c571 Mon Sep 17 00:00:00 2001
From: coolusergit1 <143963070+coolusergit1@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:39:16 +0200
Subject: [PATCH 212/232] Create PriorityQueue fallback (< .NET 6)

---
 C#/Searching/PriorityQueue fallback (< .NET 6) | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)
 create mode 100644 C#/Searching/PriorityQueue fallback (< .NET 6)

diff --git a/C#/Searching/PriorityQueue fallback (< .NET 6) b/C#/Searching/PriorityQueue fallback (< .NET 6)
new file mode 100644
index 0000000..85c13f4
--- /dev/null
+++ b/C#/Searching/PriorityQueue fallback (< .NET 6)	
@@ -0,0 +1,18 @@
+using System.Collections.Generic;
+
+public class MinPQ<T>
+{
+    readonly List<(T item,double pri)> h = new();
+    public int Count => h.Count;
+    public void Enqueue(T item, double pri) { h.Add((item,pri)); SiftUp(h.Count-1); }
+    public bool TryDequeue(out T item, out double pri)
+    {
+        if (h.Count == 0) { item = default; pri = 0; return false; }
+        (item, pri) = h[0];
+        var last = h[^1]; h.RemoveAt(h.Count-1);
+        if (h.Count > 0) { h[0] = last; SiftDown(0); }
+        return true;
+    }
+    void SiftUp(int i){ while(i>0){int p=(i-1)/2; if(h[p].pri<=h[i].pri) break; (h[p],h[i])=(h[i],h[p]); i=p; } }
+    void SiftDown(int i){ for(;;){int l=i*2+1,r=l+1,sm=i; if(l<h.Count&&h[l].pri<h[sm].pri) sm=l; if(r<h.Count&&h[r].pri<h[sm].pri) sm=r; if(sm==i) break; (h[i],h[sm])=(h[sm],h[i]); i=sm; } }
+}

From 9a073f1417869630b5819401f13f0d3454502350 Mon Sep 17 00:00:00 2001
From: coolusergit1 <143963070+coolusergit1@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:40:19 +0200
Subject: [PATCH 213/232] =?UTF-8?q?Create=20k-d=20Tree=20=E2=80=94=20neare?=
 =?UTF-8?q?st=20neighbor=20(Euclidean,=202D)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...\200\224 nearest neighbor (Euclidean, 2D)" | 36 +++++++++++++++++++
 1 file changed, 36 insertions(+)
 create mode 100644 "C#/Searching/k-d Tree \342\200\224 nearest neighbor (Euclidean, 2D)"

diff --git "a/C#/Searching/k-d Tree \342\200\224 nearest neighbor (Euclidean, 2D)" "b/C#/Searching/k-d Tree \342\200\224 nearest neighbor (Euclidean, 2D)"
new file mode 100644
index 0000000..68f6c7d
--- /dev/null
+++ "b/C#/Searching/k-d Tree \342\200\224 nearest neighbor (Euclidean, 2D)"	
@@ -0,0 +1,36 @@
+using System;
+
+public class KDNode {
+    public double X, Y; public KDNode Left, Right;
+    public KDNode(double x, double y) { X = x; Y = y; }
+}
+
+public static class KDTree {
+    public static KDNode Build((double x,double y)[] pts, int l=0, int r=-1, int depth=0) {
+        if (r==-1) r = pts.Length-1;
+        if (l>r) return null;
+        int m=(l+r)/2; bool splitX = (depth%2)==0;
+        Array.Sort(pts, l, r-l+1, Comparer<(double,double)>.Create((a,b) =>
+            splitX ? a.x.CompareTo(b.x) : a.y.CompareTo(b.y)));
+        var node = new KDNode(pts[m].x, pts[m].y);
+        node.Left  = Build(pts, l, m-1, depth+1);
+        node.Right = Build(pts, m+1, r, depth+1);
+        return node;
+    }
+
+    public static (double x,double y,double d2) Nearest(KDNode root, double qx, double qy) {
+        (double x,double y,double d2) best = (double.NaN,double.NaN,double.PositiveInfinity);
+        void Dfs(KDNode n, int depth){
+            if (n==null) return;
+            double d2 = (n.X-qx)*(n.X-qx) + (n.Y-qy)*(n.Y-qy);
+            if (d2 < best.d2) best = (n.X,n.Y,d2);
+            bool splitX = (depth%2)==0;
+            double delta = splitX ? qx - n.X : qy - n.Y;
+            var first  = delta<0 ? n.Left : n.Right;
+            var second = delta<0 ? n.Right: n.Left;
+            Dfs(first, depth+1);
+            if (delta*delta < best.d2) Dfs(second, depth+1);
+        }
+        Dfs(root,0); return best;
+    }
+}

From 5e81f6ffe8e432764000d096f5ae18421f120789 Mon Sep 17 00:00:00 2001
From: coolusergit1 <143963070+coolusergit1@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:40:21 +0200
Subject: [PATCH 214/232] =?UTF-8?q?Create=20BK-Tree=20=E2=80=94=20fuzzy=20?=
 =?UTF-8?q?string=20search=20by=20edit=20distance=20(Levenshtein)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...ing search by edit distance (Levenshtein)" | 46 +++++++++++++++++++
 1 file changed, 46 insertions(+)
 create mode 100644 "C#/Searching/BK-Tree \342\200\224 fuzzy string search by edit distance (Levenshtein)"

diff --git "a/C#/Searching/BK-Tree \342\200\224 fuzzy string search by edit distance (Levenshtein)" "b/C#/Searching/BK-Tree \342\200\224 fuzzy string search by edit distance (Levenshtein)"
new file mode 100644
index 0000000..2ab946f
--- /dev/null
+++ "b/C#/Searching/BK-Tree \342\200\224 fuzzy string search by edit distance (Levenshtein)"	
@@ -0,0 +1,46 @@
+using System;
+using System.Collections.Generic;
+
+public class BKNode {
+    public string Word;
+    public Dictionary<int,BKNode> Children = new();
+    public BKNode(string w) { Word = w; }
+}
+
+public class BKTree {
+    BKNode _root;
+    public void Insert(string w) {
+        if (_root==null){ _root = new BKNode(w); return; }
+        var cur=_root;
+        while(true){
+            int d = EditDistance(cur.Word, w);
+            if (!cur.Children.TryGetValue(d, out var nxt)) { cur.Children[d]=new BKNode(w); break; }
+            cur = nxt;
+        }
+    }
+    public IEnumerable<string> Search(string q, int tol) {
+        var res = new List<string>();
+        void Dfs(BKNode n){
+            int d = EditDistance(n.Word, q);
+            if (d<=tol) res.Add(n.Word);
+            for (int k=d-tol; k<=d+tol; k++)
+                if (n.Children.TryGetValue(k, out var c)) Dfs(c);
+        }
+        if (_root!=null) Dfs(_root);
+        return res;
+    }
+
+    static int EditDistance(string a, string b){
+        int n=a.Length,m=b.Length; var dp=new int[m+1];
+        for(int j=0;j<=m;j++) dp[j]=j;
+        for(int i=1;i<=n;i++){
+            int prev=dp[0]; dp[0]=i;
+            for(int j=1;j<=m;j++){
+                int tmp=dp[j];
+                dp[j]=a[i-1]==b[j-1]? prev : 1+Math.Min(prev, Math.Min(dp[j], dp[j-1]));
+                prev=tmp;
+            }
+        }
+        return dp[m];
+    }
+}

From f3ccf2547cb98dad3b9db2f6db34140399a1a889 Mon Sep 17 00:00:00 2001
From: gitguru007 <143963251+gitguru007@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:46:25 +0200
Subject: [PATCH 215/232] =?UTF-8?q?Create=20Bloom=20Filter=20=E2=80=94=20f?=
 =?UTF-8?q?ast=20probabilistic=20membership=20(no=20false=20negatives)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...abilistic membership (no false negatives)" | 32 +++++++++++++++++++
 1 file changed, 32 insertions(+)
 create mode 100644 "C#/Searching/Bloom Filter \342\200\224 fast probabilistic membership (no false negatives)"

diff --git "a/C#/Searching/Bloom Filter \342\200\224 fast probabilistic membership (no false negatives)" "b/C#/Searching/Bloom Filter \342\200\224 fast probabilistic membership (no false negatives)"
new file mode 100644
index 0000000..57d854d
--- /dev/null
+++ "b/C#/Searching/Bloom Filter \342\200\224 fast probabilistic membership (no false negatives)"	
@@ -0,0 +1,32 @@
+using System;
+using System.Security.Cryptography;
+
+public class BloomFilter {
+    private readonly BitArray _bits;
+    private readonly int _k; // hash functions
+    public BloomFilter(int sizeBits, int hashFunctions=3) { _bits=new BitArray(sizeBits); _k=hashFunctions; }
+
+    public void Add(string s){
+        foreach (int h in Hashes(s)) _bits[Pos(h)] = true;
+    }
+    public bool MightContain(string s){
+        foreach (int h in Hashes(s)) if (!_bits[Pos(h)]) return false;
+        return true;
+    }
+    int Pos(int h) => Math.Abs(h % _bits.Length);
+
+    int[] Hashes(string s){
+        // derive k hashes via SHA256 chunks
+        using var sha = SHA256.Create();
+        var bytes = sha.ComputeHash(System.Text.Encoding.UTF8.GetBytes(s));
+        var hs = new int[_k];
+        for(int i=0;i<_k;i++) hs[i] = BitConverter.ToInt32(bytes, (i*4) % bytes.Length);
+        return hs;
+    }
+
+    class BitArray {
+        readonly int[] a; public int Length {get;}
+        public BitArray(int bits){ Length=bits; a=new int[(bits+31)/32]; }
+        public bool this[int i] { get => (a[i>>5] & (1<<(i&31)))!=0; set { if(value) a[i>>5]|=1<<(i&31); else a[i>>5]&=~(1<<(i&31)); } }
+    }
+}

From cb12f849725645f8288edf589629189e7afa735a Mon Sep 17 00:00:00 2001
From: gitguru007 <143963251+gitguru007@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:46:27 +0200
Subject: [PATCH 216/232] =?UTF-8?q?Create=20Cuckoo=20Hash=20(two=20tables,?=
 =?UTF-8?q?=20two=20hashes)=20=E2=80=94=20O(1)=20lookup,=20high=20load=20f?=
 =?UTF-8?q?actor?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...342\200\224 O(1) lookup, high load factor" | 32 +++++++++++++++++++
 1 file changed, 32 insertions(+)
 create mode 100644 "C#/Searching/Cuckoo Hash (two tables, two hashes) \342\200\224 O(1) lookup, high load factor"

diff --git "a/C#/Searching/Cuckoo Hash (two tables, two hashes) \342\200\224 O(1) lookup, high load factor" "b/C#/Searching/Cuckoo Hash (two tables, two hashes) \342\200\224 O(1) lookup, high load factor"
new file mode 100644
index 0000000..c3d1c4b
--- /dev/null
+++ "b/C#/Searching/Cuckoo Hash (two tables, two hashes) \342\200\224 O(1) lookup, high load factor"	
@@ -0,0 +1,32 @@
+using System;
+
+public class CuckooHash {
+    private readonly string[] T1, T2;
+    private readonly int _cap; private readonly int _maxKicks;
+
+    public CuckooHash(int capacity=1024, int maxKicks=32){ _cap=capacity; T1=new string[_cap]; T2=new string[_cap]; _maxKicks=maxKicks; }
+
+    int H1(string s)=> Math.Abs(s.GetHashCode()) % _cap;
+    int H2(string s)=> Math.Abs(HashCode.Combine(146959810, s.GetHashCode())) % _cap;
+
+    public bool Insert(string key){
+        if (Contains(key)) return true;
+        string cur = key; int table = 1;
+        for(int k=0;k<_maxKicks;k++){
+            if (table==1){
+                int i=H1(cur);
+                if (T1[i]==null){ T1[i]=cur; return true; }
+                (cur, T1[i]) = (T1[i], cur); table=2;
+            } else {
+                int j=H2(cur);
+                if (T2[j]==null){ T2[j]=cur; return true; }
+                (cur, T2[j]) = (T2[j], cur); table=1;
+            }
+        }
+        return false; // rehash needed in real impl
+    }
+
+    public bool Contains(string key){
+        return T1[H1(key)]==key || T2[H2(key)]==key;
+    }
+}

From 8ee337cbf1d8b56364a132cdd0e4e34e2a09a3b2 Mon Sep 17 00:00:00 2001
From: gitguru007 <143963251+gitguru007@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:46:29 +0200
Subject: [PATCH 217/232] =?UTF-8?q?Create=20Suffix=20Array=20=E2=80=94=20s?=
 =?UTF-8?q?ubstring=20search=20via=20binary=20search?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...\200\224 substring search via binary search" | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)
 create mode 100644 "C#/Searching/Suffix Array \342\200\224 substring search via binary search"

diff --git "a/C#/Searching/Suffix Array \342\200\224 substring search via binary search" "b/C#/Searching/Suffix Array \342\200\224 substring search via binary search"
new file mode 100644
index 0000000..4716d01
--- /dev/null
+++ "b/C#/Searching/Suffix Array \342\200\224 substring search via binary search"	
@@ -0,0 +1,17 @@
+using System;
+using System.Linq;
+
+public class SuffixArray {
+    private readonly string s; private readonly int[] sa;
+    public SuffixArray(string s){ this.s=s; sa=Enumerable.Range(0,s.Length).OrderBy(i=>s[i..]).ToArray(); }
+    public bool Contains(string pat){
+        int l=0,r=sa.Length-1;
+        while(l<=r){
+            int m=(l+r)/2;
+            int cmp = string.Compare(s.AsSpan(sa[m], Math.Min(pat.Length, s.Length-sa[m])), pat, StringComparison.Ordinal);
+            if (cmp==0) return true;
+            if (cmp<0) l=m+1; else r=m-1;
+        }
+        return false;
+    }
+}

From 8f083f74fbe8e173a757e2af2a06cfd246c99b4a Mon Sep 17 00:00:00 2001
From: gitguru007 <143963251+gitguru007@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:46:30 +0200
Subject: [PATCH 218/232] =?UTF-8?q?Create=20Grid=20A*=20(8-dir)=20?=
 =?UTF-8?q?=E2=80=94=20practical=20pathfinding=20with=20obstacles?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...\224 practical pathfinding with obstacles" | 45 +++++++++++++++++++
 1 file changed, 45 insertions(+)
 create mode 100644 "C#/Searching/Grid A* (8-dir) \342\200\224 practical pathfinding with obstacles"

diff --git "a/C#/Searching/Grid A* (8-dir) \342\200\224 practical pathfinding with obstacles" "b/C#/Searching/Grid A* (8-dir) \342\200\224 practical pathfinding with obstacles"
new file mode 100644
index 0000000..2ac4822
--- /dev/null
+++ "b/C#/Searching/Grid A* (8-dir) \342\200\224 practical pathfinding with obstacles"	
@@ -0,0 +1,45 @@
+using System;
+using System.Collections.Generic;
+
+public static class GridAStar {
+    static readonly (int dx,int dy,double w)[] Dir = {
+        (1,0,1),( -1,0,1),(0,1,1),(0,-1,1),
+        (1,1,Math.Sqrt(2)), (1,-1,Math.Sqrt(2)), (-1,1,Math.Sqrt(2)), (-1,-1,Math.Sqrt(2))
+    };
+
+    public static List<(int x,int y)> Find(bool[,] blocked, (int x,int y) s, (int x,int y) g) {
+        int n=blocked.GetLength(0), m=blocked.GetLength(1);
+        double Heu(int x,int y)=> Math.Max(Math.Abs(x-g.x), Math.Abs(y-g.y)); // Chebyshev
+
+        var gScore = new Dictionary<(int,int),double> { [s]=0 };
+        var parent = new Dictionary<(int,int),(int,int)>();
+        var open = new PriorityQueue<(int,int),double>(); open.Enqueue(s, Heu(s.x,s.y));
+        var closed = new HashSet<(int,int)>();
+
+        bool In(int x,int y)=> x>=0&&x<n&&y>=0&&y<m&&!blocked[x,y];
+
+        while(open.Count>0){
+            open.TryDequeue(out var u, out _);
+            if (u.Equals(g)) return Reconstruct(s,g,parent);
+            if (!closed.Add(u)) continue;
+
+            foreach (var (dx,dy,w) in Dir){
+                int vx=u.Item1+dx, vy=u.Item2+dy;
+                if (!In(vx,vy)) continue;
+                double ng = gScore[u] + w;
+                if (!gScore.TryGetValue((vx,vy), out double gv) || ng < gv){
+                    gScore[(vx,vy)]=ng;
+                    parent[(vx,vy)]=u;
+                    open.Enqueue((vx,vy), ng + Heu(vx,vy));
+                }
+            }
+        }
+        return new();
+
+        static List<(int,int)> Reconstruct((int,int) s, (int,int) g, Dictionary<(int,int),(int,int)> p){
+            var path = new List<(int,int)>{ g };
+            while(!path[^1].Equals(s)) path.Add(p[path[^1]]);
+            path.Reverse(); return path;
+        }
+    }
+}

From 4c7f565a3c2d557ce4a603d387399e023cb47f9e Mon Sep 17 00:00:00 2001
From: gitguru007 <143963251+gitguru007@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:46:31 +0200
Subject: [PATCH 219/232] =?UTF-8?q?Create=20LRU=20Cache=20(hash=20+=20link?=
 =?UTF-8?q?ed=20list)=20=E2=80=94=20fast=20=E2=80=9Csearch=20with=20evicti?=
 =?UTF-8?q?on=E2=80=9D?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...2\200\234search with eviction\342\200\235" | 25 +++++++++++++++++++
 1 file changed, 25 insertions(+)
 create mode 100644 "C#/Searching/LRU Cache (hash + linked list) \342\200\224 fast \342\200\234search with eviction\342\200\235"

diff --git "a/C#/Searching/LRU Cache (hash + linked list) \342\200\224 fast \342\200\234search with eviction\342\200\235" "b/C#/Searching/LRU Cache (hash + linked list) \342\200\224 fast \342\200\234search with eviction\342\200\235"
new file mode 100644
index 0000000..23cd0d6
--- /dev/null
+++ "b/C#/Searching/LRU Cache (hash + linked list) \342\200\224 fast \342\200\234search with eviction\342\200\235"	
@@ -0,0 +1,25 @@
+using System.Collections.Generic;
+
+public class LRUCache<K,V> {
+    readonly int _cap;
+    readonly Dictionary<K, LinkedListNode<(K key,V val)>> _map = new();
+    readonly LinkedList<(K key,V val)> _list = new();
+    public LRUCache(int capacity){ _cap = capacity; }
+
+    public bool TryGet(K k, out V v){
+        if (_map.TryGetValue(k, out var node)){
+            _list.Remove(node); _list.AddFirst(node); v = node.Value.val; return true;
+        }
+        v = default!; return false;
+    }
+    public void Put(K k, V v){
+        if (_map.TryGetValue(k, out var node)){
+            node.Value = (k,v); _list.Remove(node); _list.AddFirst(node);
+        } else {
+            if (_map.Count == _cap){
+                var tail = _list.Last; _map.Remove(tail.Value.key); _list.RemoveLast();
+            }
+            var nn = new LinkedListNode<(K,V)>((k,v)); _list.AddFirst(nn); _map[k]=nn;
+        }
+    }
+}

From 15fc0f9e2568150f6ed4d77baf96f8c4e335659d Mon Sep 17 00:00:00 2001
From: gitguru007 <143963251+gitguru007@users.noreply.github.com>
Date: Thu, 2 Oct 2025 11:46:33 +0200
Subject: [PATCH 220/232] Create Locality-Sensitive Hashing (MinHash sketch)

---
 ...ocality-Sensitive Hashing (MinHash sketch) | 32 +++++++++++++++++++
 1 file changed, 32 insertions(+)
 create mode 100644 C#/Searching/Locality-Sensitive Hashing (MinHash sketch)

diff --git a/C#/Searching/Locality-Sensitive Hashing (MinHash sketch) b/C#/Searching/Locality-Sensitive Hashing (MinHash sketch)
new file mode 100644
index 0000000..230d847
--- /dev/null
+++ b/C#/Searching/Locality-Sensitive Hashing (MinHash sketch)	
@@ -0,0 +1,32 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+
+public static class MinHashLSH {
+    // Each set -> signature of k hashes; banding groups signatures to buckets
+    public static int[] MinHashSignature(HashSet<int> set, (int a,int b,int p)[] funcs, int m){
+        int k = funcs.Length; var sig = new int[k];
+        for (int i=0;i<k;i++){
+            int best=int.MaxValue, a=funcs[i].a, b=funcs[i].b, p=funcs[i].p;
+            foreach (int x in set) best = Math.Min(best, (a*x + b) % p);
+            sig[i] = best % m;
+        }
+        return sig;
+    }
+
+    public static Dictionary<string,List<string>> LSHBuckets(Dictionary<string,HashSet<int>> items,
+        (int a,int b,int p)[] funcs, int m, int bands, int rowsPerBand)
+    {
+        var buckets = new Dictionary<string,List<string>>();
+        foreach (var (id, set) in items){
+            var sig = MinHashSignature(set, funcs, m);
+            for (int b=0;b<bands;b++){
+                var slice = string.Join(",", sig.Skip(b*rowsPerBand).Take(rowsPerBand));
+                string key = $"b{b}:{slice}";
+                if (!buckets.ContainsKey(key)) buckets[key] = new();
+                buckets[key].Add(id);
+            }
+        }
+        return buckets; // candidates share a bucket
+    }
+}

From 34fb8b9e9c456c14e0274c45206d2cd5155ce12b Mon Sep 17 00:00:00 2001
From: opensource-guru <144161983+opensource-guru@users.noreply.github.com>
Date: Thu, 2 Oct 2025 12:09:01 +0200
Subject: [PATCH 221/232] =?UTF-8?q?Create=20Bellman=E2=80=93Ford?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 "C#/Searching/Bellman\342\200\223Ford" | 34 ++++++++++++++++++++++++++
 1 file changed, 34 insertions(+)
 create mode 100644 "C#/Searching/Bellman\342\200\223Ford"

diff --git "a/C#/Searching/Bellman\342\200\223Ford" "b/C#/Searching/Bellman\342\200\223Ford"
new file mode 100644
index 0000000..bb8422f
--- /dev/null
+++ "b/C#/Searching/Bellman\342\200\223Ford"
@@ -0,0 +1,34 @@
+using System;
+using System.Collections.Generic;
+
+public static class BellmanFord
+{
+    public static (Dictionary<int,double> dist, bool hasNegCycle)
+    Solve(IEnumerable<(int u,int v,double w)> edges, IEnumerable<int> nodes, int s)
+    {
+        var INF = double.PositiveInfinity;
+        var dist = new Dictionary<int,double>();
+        foreach (var n in nodes) dist[n] = INF;
+        dist[s] = 0;
+
+        int N = 0; foreach (var _ in nodes) N++;
+
+        // Relax edges N-1 times
+        for (int i=0; i<N-1; i++)
+        {
+            bool changed = false;
+            foreach (var (u,v,w) in edges)
+            {
+                if (dist[u] + w < dist[v])
+                { dist[v] = dist[u] + w; changed = true; }
+            }
+            if (!changed) break;
+        }
+
+        // Detect negative cycle
+        foreach (var (u,v,w) in edges)
+            if (dist[u] + w < dist[v]) return (dist, true);
+
+        return (dist, false);
+    }
+}

From aa2a0e0ab999c808eaef27836533706f356b8457 Mon Sep 17 00:00:00 2001
From: opensource-guru <144161983+opensource-guru@users.noreply.github.com>
Date: Thu, 2 Oct 2025 12:10:33 +0200
Subject: [PATCH 222/232] =?UTF-8?q?Create=20Floyd=E2=80=93Warshall=20(all-?=
 =?UTF-8?q?pairs=20shortest=20paths)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...00\223Warshall (all-pairs shortest paths)" | 22 +++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 "C#/Searching/Floyd\342\200\223Warshall (all-pairs shortest paths)"

diff --git "a/C#/Searching/Floyd\342\200\223Warshall (all-pairs shortest paths)" "b/C#/Searching/Floyd\342\200\223Warshall (all-pairs shortest paths)"
new file mode 100644
index 0000000..31bcc99
--- /dev/null
+++ "b/C#/Searching/Floyd\342\200\223Warshall (all-pairs shortest paths)"	
@@ -0,0 +1,22 @@
+using System;
+using System.Collections.Generic;
+
+public static class FloydWarshall
+{
+    public static double[,] APSP(int n, IEnumerable<(int u,int v,double w)> edges)
+    {
+        var INF = 1e18;
+        var d = new double[n,n];
+        for (int i=0;i<n;i++)
+            for (int j=0;j<n;j++)
+                d[i,j] = (i==j) ? 0 : INF;
+
+        foreach (var (u,v,w) in edges) d[u,v] = Math.Min(d[u,v], w);
+
+        for (int k=0;k<n;k++)
+            for (int i=0;i<n;i++)
+                for (int j=0;j<n;j++)
+                    if (d[i,k] + d[k,j] < d[i,j]) d[i,j] = d[i,k] + d[k,j];
+        return d;
+    }
+}

From 3c6077583a84d4d8ae313572d011ba9d3e445533 Mon Sep 17 00:00:00 2001
From: opensource-guru <144161983+opensource-guru@users.noreply.github.com>
Date: Thu, 2 Oct 2025 12:10:34 +0200
Subject: [PATCH 223/232] =?UTF-8?q?Create=20Johnson=E2=80=99s=20Algorithm?=
 =?UTF-8?q?=20(APSP=20on=20sparse=20graphs,=20handles=20negatives=20if=20n?=
 =?UTF-8?q?o=20neg=20cycles)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...aphs, handles negatives if no neg cycles)" | 64 +++++++++++++++++++
 1 file changed, 64 insertions(+)
 create mode 100644 "C#/Searching/Johnson\342\200\231s Algorithm (APSP on sparse graphs, handles negatives if no neg cycles)"

diff --git "a/C#/Searching/Johnson\342\200\231s Algorithm (APSP on sparse graphs, handles negatives if no neg cycles)" "b/C#/Searching/Johnson\342\200\231s Algorithm (APSP on sparse graphs, handles negatives if no neg cycles)"
new file mode 100644
index 0000000..f54fb98
--- /dev/null
+++ "b/C#/Searching/Johnson\342\200\231s Algorithm (APSP on sparse graphs, handles negatives if no neg cycles)"	
@@ -0,0 +1,64 @@
+using System;
+using System.Collections.Generic;
+
+public static class Johnson
+{
+    public static (Dictionary<int,Dictionary<int,double>> dist, bool negCycle)
+    APSP(Dictionary<int,List<(int to,double w)>> adj)
+    {
+        // Add super-source q -> all nodes with 0 weight
+        var nodes = new List<int>(adj.Keys);
+        int q = int.MinValue; adj[q] = new();
+        foreach (var v in nodes) adj[q].Add((v, 0));
+
+        // Bellman–Ford potentials h
+        var edges = new List<(int u,int v,double w)>();
+        foreach (var (u, list) in adj) foreach (var (v,w) in list) edges.Add((u,v,w));
+
+        var (h, hasNeg) = BellmanFord.Solve(edges, adj.Keys, q);
+        if (hasNeg) { adj.Remove(q); return (new(), true); }
+
+        // Reweight edges w' = w + h[u] - h[v]
+        var re = new Dictionary<int,List<(int to,double w)>>();
+        foreach (var u in adj.Keys) re[u] = new();
+        foreach (var (u, list) in adj)
+            foreach (var (v,w) in list)
+                if (u != q) re[u].Add((v, w + h[u] - h[v]));
+        re.Remove(q); adj.Remove(q);
+
+        // Run Dijkstra from each node
+        var all = new Dictionary<int,Dictionary<int,double>>();
+        foreach (var s in re.Keys)
+        {
+            var (dist, _) = Dijkstra.ShortestPaths(new Graph{ Adj = re }, s);
+            // Undo reweight: d(u,v) = d'(u,v) - h[u] + h[v]
+            var fixedD = new Dictionary<int,double>();
+            foreach (var v in dist.Keys)
+                fixedD[v] = double.IsPositiveInfinity(dist[v]) ? dist[v] : dist[v] - h[s] + h[v];
+            all[s] = fixedD;
+        }
+        return (all, false);
+    }
+}
+
+// Minimal Graph + Dijkstra re-use
+public class Graph { public Dictionary<int,List<(int to,double w)>> Adj = new(); }
+public static class Dijkstra {
+    public static (Dictionary<int,double> dist, Dictionary<int,int> parent)
+    ShortestPaths(Graph g, int start) {
+        var dist = new Dictionary<int,double>();
+        var parent = new Dictionary<int,int>();
+        foreach (var u in g.Adj.Keys) dist[u] = double.PositiveInfinity;
+        dist[start] = 0;
+        var pq = new PriorityQueue<int,double>(); pq.Enqueue(start, 0);
+        while (pq.Count>0) {
+            pq.TryDequeue(out int u, out double du);
+            if (du != dist[u]) continue;
+            foreach (var (v,w) in g.Adj[u]) {
+                double nd = du + w;
+                if (nd < dist[v]) { dist[v] = nd; parent[v] = u; pq.Enqueue(v, nd); }
+            }
+        }
+        return (dist, parent);
+    }
+}

From c733d0d126a9a2c107671ea99cbd7708bde9efa1 Mon Sep 17 00:00:00 2001
From: opensource-guru <144161983+opensource-guru@users.noreply.github.com>
Date: Thu, 2 Oct 2025 12:10:36 +0200
Subject: [PATCH 224/232] Create Multi-Source BFS (shortest distance to any
 seed)

---
 ...Source BFS (shortest distance to any seed) | 20 +++++++++++++++++++
 1 file changed, 20 insertions(+)
 create mode 100644 C#/Searching/Multi-Source BFS (shortest distance to any seed)

diff --git a/C#/Searching/Multi-Source BFS (shortest distance to any seed) b/C#/Searching/Multi-Source BFS (shortest distance to any seed)
new file mode 100644
index 0000000..2e30b1e
--- /dev/null
+++ b/C#/Searching/Multi-Source BFS (shortest distance to any seed)	
@@ -0,0 +1,20 @@
+using System.Collections.Generic;
+
+public static class MultiSourceBFS
+{
+    public static Dictionary<int,int> Dist(Dictionary<int,List<int>> adj, IEnumerable<int> sources)
+    {
+        var dist = new Dictionary<int,int>();
+        var q = new Queue<int>();
+        foreach (var s in sources) { dist[s] = 0; q.Enqueue(s); }
+
+        while (q.Count > 0)
+        {
+            int u = q.Dequeue();
+            foreach (var v in adj[u])
+                if (!dist.ContainsKey(v))
+                { dist[v] = dist[u] + 1; q.Enqueue(v); }
+        }
+        return dist;
+    }
+}

From eea97040883f865fd4d653f2acf7a2071df81b00 Mon Sep 17 00:00:00 2001
From: opensource-guru <144161983+opensource-guru@users.noreply.github.com>
Date: Thu, 2 Oct 2025 12:10:37 +0200
Subject: [PATCH 225/232] =?UTF-8?q?Create=20Disjoint=20Set=20Union=20(Unio?=
 =?UTF-8?q?n-Find)=20=E2=80=94=20fast=20connectivity=20queries?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...ion-Find) \342\200\224 fast connectivity queries" | 12 ++++++++++++
 1 file changed, 12 insertions(+)
 create mode 100644 "C#/Searching/Disjoint Set Union (Union-Find) \342\200\224 fast connectivity queries"

diff --git "a/C#/Searching/Disjoint Set Union (Union-Find) \342\200\224 fast connectivity queries" "b/C#/Searching/Disjoint Set Union (Union-Find) \342\200\224 fast connectivity queries"
new file mode 100644
index 0000000..8662c02
--- /dev/null
+++ "b/C#/Searching/Disjoint Set Union (Union-Find) \342\200\224 fast connectivity queries"	
@@ -0,0 +1,12 @@
+public class DSU
+{
+    private readonly int[] p, r;
+    public DSU(int n){ p=new int[n]; r=new int[n]; for(int i=0;i<n;i++) p[i]=i; }
+    public int Find(int x){ return p[x]==x ? x : p[x]=Find(p[x]); }
+    public bool Union(int a,int b){
+        a=Find(a); b=Find(b); if (a==b) return false;
+        if (r[a]<r[b]) (a,b)=(b,a);
+        p[b]=a; if (r[a]==r[b]) r[a]++; return true;
+    }
+    public bool Connected(int a,int b)=> Find(a)==Find(b);
+}

From 2060cb37fb24c7c4de456cd67d38689505a52e8f Mon Sep 17 00:00:00 2001
From: opensource-guru <144161983+opensource-guru@users.noreply.github.com>
Date: Thu, 2 Oct 2025 12:10:38 +0200
Subject: [PATCH 226/232] =?UTF-8?q?Create=20Ternary=20Search=20Tree=20(TST?=
 =?UTF-8?q?)=20=E2=80=94=20memory-efficient=20string=20search?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...42\200\224 memory-efficient string search" | 33 +++++++++++++++++++
 1 file changed, 33 insertions(+)
 create mode 100644 "C#/Searching/Ternary Search Tree (TST) \342\200\224 memory-efficient string search"

diff --git "a/C#/Searching/Ternary Search Tree (TST) \342\200\224 memory-efficient string search" "b/C#/Searching/Ternary Search Tree (TST) \342\200\224 memory-efficient string search"
new file mode 100644
index 0000000..fc97466
--- /dev/null
+++ "b/C#/Searching/Ternary Search Tree (TST) \342\200\224 memory-efficient string search"	
@@ -0,0 +1,33 @@
+public class TstNode {
+    public char C; public bool End;
+    public TstNode Lo, Eq, Hi;
+    public TstNode(char c){ C=c; }
+}
+
+public class TernarySearchTree
+{
+    private TstNode root;
+
+    public void Insert(string s){ root = Insert(root, s, 0); }
+    private TstNode Insert(TstNode n, string s, int i){
+        char c = s[i];
+        if (n==null) n = new TstNode(c);
+        if (c < n.C) n.Lo = Insert(n.Lo, s, i);
+        else if (c > n.C) n.Hi = Insert(n.Hi, s, i);
+        else if (i+1 < s.Length) n.Eq = Insert(n.Eq, s, i+1);
+        else n.End = true;
+        return n;
+    }
+
+    public bool Search(string s){
+        var n = root; int i=0;
+        while (n!=null){
+            char c = s[i];
+            if (c < n.C) n = n.Lo;
+            else if (c > n.C) n = n.Hi;
+            else if (++i == s.Length) return n.End;
+            else n = n.Eq;
+        }
+        return false;
+    }
+}

From 4ff5e603ce27dcaef24103dbfcede825bb2e41e7 Mon Sep 17 00:00:00 2001
From: hitpack-guru <144269983+hitpack-guru@users.noreply.github.com>
Date: Thu, 2 Oct 2025 13:56:38 +0200
Subject: [PATCH 227/232] Create Bidirectional A*

---
 Bidirectional A* | 60 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 60 insertions(+)
 create mode 100644 Bidirectional A*

diff --git a/Bidirectional A* b/Bidirectional A*
new file mode 100644
index 0000000..dc7c644
--- /dev/null
+++ b/Bidirectional A*	
@@ -0,0 +1,60 @@
+using System;
+using System.Collections.Generic;
+
+public static class BiAStar
+{
+    public static List<int> Find(
+        Dictionary<int,List<(int to,double w)>> adj,
+        int s, int t, Func<int,double> hF, Func<int,double> hB)
+    {
+        var INF = double.PositiveInfinity;
+        var gF = new Dictionary<int,double>(); var gB = new Dictionary<int,double>();
+        var pF = new Dictionary<int,int>();   var pB = new Dictionary<int,int>();
+        foreach (var u in adj.Keys){ gF[u]=INF; gB[u]=INF; }
+        gF[s]=0; gB[t]=0;
+
+        var oF = new PriorityQueue<int,double>(); var oB = new PriorityQueue<int,double>();
+        oF.Enqueue(s, hF(s)); oB.Enqueue(t, hB(t));
+        var visF = new HashSet<int>(); var visB = new HashSet<int>();
+        double best = INF; int meet = -1;
+
+        while (oF.Count>0 && oB.Count>0)
+        {
+            Expand(adj, oF, gF, gB, pF, visF, hF, ref best, ref meet, forward:true);
+            if (meet!=-1) break;
+            Expand(adj, oB, gB, gF, pB, visB, hB, ref best, ref meet, forward:false);
+            if (meet!=-1) break;
+        }
+        if (meet==-1) return new();
+
+        // Reconstruct s -> meet -> t
+        var left = new List<int>(); for (int x=meet; x!=s; x=pF[x]) left.Add(x); left.Add(s); left.Reverse();
+        var right = new List<int>(); for (int x=meet; x!=t; x=pB[x]) right.Add(x); right.Add(t);
+        left.AddRange(right.GetRange(1, right.Count-1));
+        return left;
+    }
+
+    static void Expand(
+        Dictionary<int,List<(int to,double w)>> adj,
+        PriorityQueue<int,double> open,
+        Dictionary<int,double> g, Dictionary<int,double> gOther,
+        Dictionary<int,int> parent, HashSet<int> done, Func<int,double> h,
+        ref double best, ref int meet, bool forward)
+    {
+        if (open.Count==0) return;
+        open.TryDequeue(out int u, out _);
+        if (!done.Add(u)) return;
+
+        if (g[u] + gOther.GetValueOrDefault(u, double.PositiveInfinity) < best)
+        { best = g[u] + gOther[u]; meet = u; }
+
+        foreach (var (v,w) in adj[u])
+        {
+            double ng = g[u] + w;
+            if (ng < g[v])
+            {
+                g[v] = ng; parent[v] = u; open.Enqueue(v, ng + h(v));
+            }
+        }
+    }
+}

From d5e2fe9911f7fd3d109e438513499ed9f5f558df Mon Sep 17 00:00:00 2001
From: hitpack-guru <144269983+hitpack-guru@users.noreply.github.com>
Date: Thu, 2 Oct 2025 13:56:40 +0200
Subject: [PATCH 228/232] Create Suffix Automaton

---
 Suffix Automaton | 40 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 40 insertions(+)
 create mode 100644 Suffix Automaton

diff --git a/Suffix Automaton b/Suffix Automaton
new file mode 100644
index 0000000..9c71ada
--- /dev/null
+++ b/Suffix Automaton	
@@ -0,0 +1,40 @@
+using System.Collections.Generic;
+
+public class SuffixAutomaton
+{
+    class St { public int Len, Link; public Dictionary<char,int> Next = new(); }
+    private readonly List<St> st = new() { new St{ Len=0, Link=-1 } };
+    private int last = 0;
+
+    public void Extend(char c)
+    {
+        int cur = st.Count; st.Add(new St{ Len = st[last].Len + 1 });
+        int p = last;
+        while (p!=-1 && !st[p].Next.ContainsKey(c)) { st[p].Next[c]=cur; p = st[p].Link; }
+        if (p==-1) st[cur].Link = 0;
+        else {
+            int q = st[p].Next[c];
+            if (st[p].Len + 1 == st[q].Len) st[cur].Link = q;
+            else {
+                int clone = st.Count; st.Add(new St{
+                    Len = st[p].Len + 1, Link = st[q].Link, Next = new Dictionary<char,int>(st[q].Next)
+                });
+                while (p!=-1 && st[p].Next.GetValueOrDefault(c) == q) { st[p].Next[c]=clone; p = st[p].Link; }
+                st[q].Link = st[cur].Link = clone;
+            }
+        }
+        last = cur;
+    }
+
+    public SuffixAutomaton(string s){ foreach (var ch in s) Extend(ch); }
+
+    public bool Contains(string t)
+    {
+        int v=0;
+        foreach (var ch in t)
+        {
+            if (!st[v].Next.TryGetValue(ch, out v)) return false;
+        }
+        return true;
+    }
+}

From 65345fd06803436b6c178d1651bd0edb28805767 Mon Sep 17 00:00:00 2001
From: hitpack-guru <144269983+hitpack-guru@users.noreply.github.com>
Date: Thu, 2 Oct 2025 13:56:41 +0200
Subject: [PATCH 229/232] Create Fringe Search

---
 Fringe Search | 46 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 46 insertions(+)
 create mode 100644 Fringe Search

diff --git a/Fringe Search b/Fringe Search
new file mode 100644
index 0000000..2c605e2
--- /dev/null
+++ b/Fringe Search	
@@ -0,0 +1,46 @@
+using System;
+using System.Collections.Generic;
+
+public static class FringeSearch
+{
+    public static List<int> FindPath(
+        Dictionary<int,List<(int to,double w)>> adj, int s, int g, Func<int,double> h)
+    {
+        var parent = new Dictionary<int,int>();
+        var gScore = new Dictionary<int,double> { [s]=0 };
+        double fLimit = h(s);
+
+        while (true)
+        {
+            double nextFLimit = double.PositiveInfinity;
+            var fringe = new Stack<int>(); fringe.Push(s);
+
+            var visitedInIter = new HashSet<int> { s };
+            while (fringe.Count > 0)
+            {
+                int u = fringe.Pop();
+                double f = gScore[u] + h(u);
+                if (f > fLimit) { nextFLimit = Math.Min(nextFLimit, f); continue; }
+                if (u == g) return Reconstruct(parent, g);
+
+                foreach (var (v,w) in adj[u])
+                {
+                    double ng = gScore[u] + w;
+                    if (!gScore.TryGetValue(v, out double gv) || ng < gv)
+                    {
+                        gScore[v] = ng; parent[v] = u;
+                        if (!visitedInIter.Contains(v)) { fringe.Push(v); visitedInIter.Add(v); }
+                    }
+                }
+            }
+            if (double.IsPositiveInfinity(nextFLimit)) return new();
+            fLimit = nextFLimit;
+        }
+
+        static List<int> Reconstruct(Dictionary<int,int> p, int t){
+            var path = new List<int>{ t };
+            while (p.ContainsKey(path[^1])) path.Add(p[path[^1]]);
+            path.Reverse(); return path;
+        }
+    }
+}

From 35dd886a147c1288a89ce81c45f85e6d8e7ff8c5 Mon Sep 17 00:00:00 2001
From: hitpack-guru <144269983+hitpack-guru@users.noreply.github.com>
Date: Thu, 2 Oct 2025 13:56:43 +0200
Subject: [PATCH 230/232] Create Meet-in-the-Middle (subset sum existence)

---
 Meet-in-the-Middle (subset sum existence) | 35 +++++++++++++++++++++++
 1 file changed, 35 insertions(+)
 create mode 100644 Meet-in-the-Middle (subset sum existence)

diff --git a/Meet-in-the-Middle (subset sum existence) b/Meet-in-the-Middle (subset sum existence)
new file mode 100644
index 0000000..c307e6c
--- /dev/null
+++ b/Meet-in-the-Middle (subset sum existence)	
@@ -0,0 +1,35 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+
+public static class MeetInTheMiddle
+{
+    public static bool SubsetSum(int[] a, int target)
+    {
+        int n = a.Length, m = n/2;
+        var left = a[..m]; var right = a[m..];
+
+        var L = AllSums(left);
+        var R = AllSums(right); R.Sort();
+
+        foreach (var x in L)
+        {
+            int need = target - x;
+            int i = R.BinarySearch(need);
+            if (i >= 0) return true;
+        }
+        return false;
+
+        static List<int> AllSums(int[] arr)
+        {
+            var sums = new List<int>();
+            int k = arr.Length, lim = 1<<k;
+            for (int mask=0; mask<lim; mask++){
+                int s = 0;
+                for (int i=0;i<k;i++) if ((mask&(1<<i))!=0) s += arr[i];
+                sums.Add(s);
+            }
+            return sums;
+        }
+    }
+}

From 8d1437b31a43ff4d4ba08e0a8aa0976bb1a9ec5b Mon Sep 17 00:00:00 2001
From: hitpack-guru <144269983+hitpack-guru@users.noreply.github.com>
Date: Thu, 2 Oct 2025 13:57:38 +0200
Subject: [PATCH 231/232] =?UTF-8?q?Create=20Skip=20List=20=E2=80=94=20orde?=
 =?UTF-8?q?red=20dictionary=20with=20O(log=20n)=20expected=20search?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ... dictionary with O(log n) expected search" | 39 +++++++++++++++++++
 1 file changed, 39 insertions(+)
 create mode 100644 "Skip List \342\200\224 ordered dictionary with O(log n) expected search"

diff --git "a/Skip List \342\200\224 ordered dictionary with O(log n) expected search" "b/Skip List \342\200\224 ordered dictionary with O(log n) expected search"
new file mode 100644
index 0000000..cc82e3e
--- /dev/null
+++ "b/Skip List \342\200\224 ordered dictionary with O(log n) expected search"	
@@ -0,0 +1,39 @@
+using System;
+using System.Collections.Generic;
+
+public class SkipListNode<TK,TV> where TK : IComparable<TK> {
+    public TK Key; public TV Val;
+    public SkipListNode<TK,TV>[] Next;
+    public SkipListNode(int level, TK k=default, TV v=default){ Key=k; Val=v; Next=new SkipListNode<TK,TV>[level]; }
+}
+
+public class SkipList<TK,TV> where TK : IComparable<TK> {
+    readonly Random R = new(); readonly int MaxL; int Level;
+    readonly SkipListNode<TK,TV> Head;
+    public SkipList(int maxLevel=16){ MaxL=maxLevel; Level=1; Head=new SkipListNode<TK,TV>(MaxL); }
+
+    int RandLevel(){ int l=1; while(l<MaxL && R.Next(2)==0) l++; return l; }
+
+    public bool TryGet(TK key, out TV val){
+        var x = Head;
+        for (int i=Level-1;i>=0;i--)
+            while (x.Next[i]!=null && x.Next[i].Key.CompareTo(key)<0) x=x.Next[i];
+        x = x.Next[0];
+        if (x!=null && x.Key.CompareTo(key)==0){ val=x.Val; return true; }
+        val=default!; return false;
+    }
+
+    public void Insert(TK key, TV val){
+        var update=new SkipListNode<TK,TV>[MaxL];
+        var x=Head;
+        for (int i=Level-1;i>=0;i--){
+            while (x.Next[i]!=null && x.Next[i].Key.CompareTo(key)<0) x=x.Next[i];
+            update[i]=x;
+        }
+        x=x.Next[0];
+        if (x!=null && x.Key.CompareTo(key)==0){ x.Val=val; return; }
+        int lvl=RandLevel(); if (lvl>Level){ for(int i=Level;i<lvl;i++) update[i]=Head; Level=lvl; }
+        var n=new SkipListNode<TK,TV>(lvl, key, val);
+        for (int i=0;i<lvl;i++){ n.Next[i]=update[i].Next[i]; update[i].Next[i]=n; }
+    }
+}

From d1ca02b56341d8e09a8526a84e73968931cde073 Mon Sep 17 00:00:00 2001
From: hitpack-guru <144269983+hitpack-guru@users.noreply.github.com>
Date: Thu, 2 Oct 2025 13:57:39 +0200
Subject: [PATCH 232/232] =?UTF-8?q?Create=20Treap=20=E2=80=94=20randomized?=
 =?UTF-8?q?=20BST=20(search=20=3D=20BST)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ...342\200\224 randomized BST (search = BST)" | 20 +++++++++++++++++++
 1 file changed, 20 insertions(+)
 create mode 100644 "Treap \342\200\224 randomized BST (search = BST)"

diff --git "a/Treap \342\200\224 randomized BST (search = BST)" "b/Treap \342\200\224 randomized BST (search = BST)"
new file mode 100644
index 0000000..8a4a1e2
--- /dev/null
+++ "b/Treap \342\200\224 randomized BST (search = BST)"	
@@ -0,0 +1,20 @@
+using System;
+
+public class TreapNode {
+    public int Key, Pri; public TreapNode L, R;
+    public TreapNode(int key, int pri){ Key=key; Pri=pri; }
+}
+public static class Treap {
+    static readonly Random R = new();
+    public static TreapNode Insert(TreapNode t, int key){
+        if (t==null) return new TreapNode(key, R.Next());
+        if (key < t.Key) { t.L = Insert(t.L, key); if (t.L.Pri < t.Pri) t = RotateRight(t); }
+        else if (key > t.Key){ t.R = Insert(t.R, key); if (t.R.Pri < t.Pri) t = RotateLeft(t); }
+        return t;
+    }
+    public static TreapNode Search(TreapNode t, int key){
+        while (t!=null && t.Key!=key) t = key < t.Key ? t.L : t.R; return t;
+    }
+    static TreapNode RotateRight(TreapNode y){ var x=y.L; y.L=x.R; x.R=y; return x; }
+    static TreapNode RotateLeft (TreapNode x){ var y=x.R; x.R=y.L; y.L=x; return y; }
+}