diff --git a/Multiple_Linear_Regression_Working_Copy.ipynb b/Multiple_Linear_Regression_Working_Copy.ipynb
new file mode 100644
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@@ -0,0 +1,357 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "toc_visible": true,
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "FB7mg-9WUSAn"
+ },
+ "source": [
+ "# Multiple Linear Regression "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zcXufiytUV3b"
+ },
+ "source": [
+ "## Importing Libraries"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "C1pbceKb-bOf"
+ },
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "x1V8I7-1Ugu8"
+ },
+ "source": [
+ "## Importing Data Set"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "_lr-eRA2-fHb"
+ },
+ "source": [
+ "dataset = pd.read_csv('Covid_data Multiple Linear Regression.csv')\n",
+ "X = dataset.iloc[:, :-1].values\n",
+ "y = dataset.iloc[:, -1].values"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "V9bZq2w39GwP"
+ },
+ "source": [
+ "## Encoding categorical data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "rrnDb2H8ALZh"
+ },
+ "source": [
+ "from sklearn.compose import ColumnTransformer\n",
+ "from sklearn.preprocessing import OneHotEncoder\n",
+ "ct = ColumnTransformer(transformers = [('encoder', OneHotEncoder(),[1])], remainder = 'passthrough')\n",
+ "X = np.array(ct.fit_transform(X))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ScHjxImAAtnb",
+ "outputId": "2116d7c4-4467-4aa8-a35e-5e71d4c8d8e4"
+ },
+ "source": [
+ "print(X)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[[0.0 0.0 1.0 10 0 97]\n",
+ " [1.0 0.0 0.0 80 0 92]\n",
+ " [0.0 1.0 0.0 10 0 97]\n",
+ " [1.0 0.0 0.0 81 4 75]\n",
+ " [0.0 0.0 1.0 12 0 97]\n",
+ " [0.0 1.0 0.0 18 0 66]\n",
+ " [0.0 0.0 1.0 13 0 93]\n",
+ " [1.0 0.0 0.0 94 4 64]\n",
+ " [1.0 0.0 0.0 86 2 76]\n",
+ " [0.0 0.0 1.0 20 0 92]\n",
+ " [0.0 0.0 1.0 15 0 94]\n",
+ " [0.0 0.0 1.0 15 0 92]\n",
+ " [0.0 1.0 0.0 16 0 92]\n",
+ " [0.0 0.0 1.0 17 0 93]\n",
+ " [0.0 0.0 1.0 18 0 93]\n",
+ " [0.0 0.0 1.0 19 0 92]\n",
+ " [0.0 0.0 1.0 19 0 89]\n",
+ " [0.0 0.0 1.0 12 0 97]\n",
+ " [0.0 0.0 1.0 20 0 93]\n",
+ " [0.0 0.0 1.0 24 0 92]\n",
+ " [0.0 0.0 1.0 25 0 93]\n",
+ " [1.0 0.0 0.0 59 2 68]\n",
+ " [1.0 0.0 0.0 63 1 66]\n",
+ " [0.0 0.0 1.0 25 1 62]\n",
+ " [1.0 0.0 0.0 26 0 94]\n",
+ " [0.0 1.0 0.0 26 0 97]\n",
+ " [0.0 0.0 1.0 28 0 99]\n",
+ " [1.0 0.0 0.0 59 0 69]\n",
+ " [0.0 0.0 1.0 28 0 89]\n",
+ " [0.0 0.0 1.0 29 0 93]\n",
+ " [0.0 0.0 1.0 48 0 93]\n",
+ " [0.0 0.0 1.0 30 0 62]\n",
+ " [0.0 0.0 1.0 30 0 86]\n",
+ " [0.0 0.0 1.0 32 0 84]\n",
+ " [0.0 0.0 1.0 34 1 90]\n",
+ " [0.0 0.0 1.0 35 0 89]\n",
+ " [1.0 0.0 0.0 50 2 77]\n",
+ " [1.0 0.0 0.0 56 2 76]\n",
+ " [1.0 0.0 0.0 56 1 77]\n",
+ " [1.0 0.0 0.0 36 1 90]\n",
+ " [1.0 0.0 0.0 38 2 75]\n",
+ " [0.0 0.0 1.0 40 0 90]\n",
+ " [1.0 0.0 0.0 40 0 92]\n",
+ " [1.0 0.0 0.0 61 2 76]\n",
+ " [1.0 0.0 0.0 42 0 92]\n",
+ " [1.0 0.0 0.0 46 0 91]\n",
+ " [0.0 0.0 1.0 49 0 80]\n",
+ " [1.0 0.0 0.0 52 1 80]\n",
+ " [0.0 0.0 1.0 32 0 89]\n",
+ " [1.0 0.0 0.0 53 1 55]\n",
+ " [1.0 0.0 0.0 54 1 70]\n",
+ " [1.0 0.0 0.0 90 3 67]\n",
+ " [1.0 0.0 0.0 78 2 71]\n",
+ " [1.0 0.0 0.0 58 0 70]\n",
+ " [1.0 0.0 0.0 60 1 68]\n",
+ " [0.0 0.0 1.0 61 0 90]\n",
+ " [1.0 0.0 0.0 62 1 68]\n",
+ " [1.0 0.0 0.0 86 3 71]\n",
+ " [1.0 0.0 0.0 62 2 69]\n",
+ " [1.0 0.0 0.0 64 1 73]\n",
+ " [1.0 0.0 0.0 68 1 67]\n",
+ " [1.0 0.0 0.0 69 0 53]\n",
+ " [0.0 0.0 1.0 70 1 88]\n",
+ " [1.0 0.0 0.0 74 2 75]\n",
+ " [1.0 0.0 0.0 75 1 72]\n",
+ " [1.0 0.0 0.0 76 4 80]\n",
+ " [0.0 0.0 1.0 35 0 92]\n",
+ " [0.0 0.0 1.0 13 0 94]\n",
+ " [1.0 0.0 0.0 82 3 73]\n",
+ " [1.0 0.0 0.0 84 3 75]]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2vBbKI1BUilB"
+ },
+ "source": [
+ "## Splitting into test and training set"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "4_S1rBJM-z8U"
+ },
+ "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 = 0.4, random_state = 0)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "K6uEL0b5Utac"
+ },
+ "source": [
+ "## Training the Multiple Linear Regression model on the Training set"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "TdY6tls4A5Q1",
+ "outputId": "eafbb709-5adb-4a72-e4bc-d67a797eb8c5"
+ },
+ "source": [
+ "from sklearn.linear_model import LinearRegression\n",
+ "regressor = LinearRegression()\n",
+ "regressor.fit(X_train, y_train)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 30
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RyRva69qUzBK"
+ },
+ "source": [
+ "## Predicting the Test set results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "oWdAcCHvFK0O",
+ "outputId": "fa3f9ea8-7d36-4a0c-c10f-fb8608b3a8ed"
+ },
+ "source": [
+ "y_pred = regressor.predict(X_test)\n",
+ "np.set_printoptions(precision= 3)\n",
+ "print(np.concatenate((y_pred.reshape(len(y_pred), 1), y_test.reshape(len(y_test),1)), 1))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[[ 98523.191 93010. ]\n",
+ " [ 65402.264 66019. ]\n",
+ " [ 91633.957 91019. ]\n",
+ " [ 59006.199 59994. ]\n",
+ " [ 83371.86 83891. ]\n",
+ " [ 37389.494 46016. ]\n",
+ " [ 30993.428 37914. ]\n",
+ " [ 61449.821 62001. ]\n",
+ " [ 88243.989 90001. ]\n",
+ " [ 44653.711 42099. ]\n",
+ " [ 60509.248 61018. ]\n",
+ " [ 94138.001 93091. ]\n",
+ " [ 81045.966 75012. ]\n",
+ " [ 87382.408 88012. ]\n",
+ " [ 89441.362 90015. ]\n",
+ " [ 73918.161 71191. ]\n",
+ " [ 45028.695 42010. ]\n",
+ " [ 64830.56 67019. ]\n",
+ " [ 62066.945 62019. ]\n",
+ " [107662.326 110911. ]\n",
+ " [ 49277.928 39010. ]\n",
+ " [ 66394.372 61082. ]\n",
+ " [110895.003 111911. ]\n",
+ " [ 76026.676 78004. ]\n",
+ " [103591.699 102019. ]\n",
+ " [ 43955.278 39010. ]\n",
+ " [ 61761.269 62019. ]\n",
+ " [ 83187.824 84008. ]]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "uziF7cmeU4tJ"
+ },
+ "source": [
+ "## Visualizing results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 265
+ },
+ "id": "5Mw6nKnxGZ7y",
+ "outputId": "610eb973-b57f-4c69-e9ea-186bbc27a2c0"
+ },
+ "source": [
+ "plt.plot(y_pred)\n",
+ "plt.plot(y_test)\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file