I've demonstrated the working of the decision tree-based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample. All the steps have been explained in detail with graphics for better understanding.

Machine Learning - End to End Data Science Projects

This project is for the Identification of Iris flower species is presented

📃🎉 Additional datasets for tensorflow.keras

An "End-to-End Machine Learning" project focuses on building a machine learning pipeline that prevents data leakage and deploys the model with microservice-architecture for real-world use.

Iris Recognition System Implemented in Python.

This repository contains the Iris Classification Machine Learning Project. Which is a comprehensive exploration of machine learning techniques applied to the classification of iris flowers into different species based on their physical characteristics.

The Iris flower classification project uses the Iris dataset to demonstrate a simple machine-learning workflow. It covers data loading, exploration, preprocessing, model building, evaluation, and data visualization.

In this repository, I classify the Iris dataset using Qutrits and IBM Quantum pulse technology.

A step-wise tutorial to demonstrate the steps required to deploy a ML model using AWS Lambda, Github Actions, API Gateway and use Streamlit to access the model API through a UI.

This is a simple Flask-based machine learning web application that predicts the species of an Iris flower based on user inputs. The model is trained using Logistic Regression and deployed on Glitch.com.

Predict Iris with mongodb 🌷

A simple static web app (HTML + CSS + JavaScript) hosted on GitHub Pages that predicts the species of an Iris flower using a Flask backend API deployed on Hugging Face Spaces.

The "KubeFlow-Pipeline-IRIS-Classifier-Demo" project is a comprehensive demonstration of building and deploying a Kubeflow Pipeline for training and deploying an IRIS classifier model. This project showcases the power of Kubeflow for creating reproducible, scalable, and efficient machine learning pipelines.

I will be implementing the famous ai/ml, iris-flower classification project