A comprehensive collection of machine learning projects spanning regression, classification, and ensemble methods. This journey covers everything from data preprocessing and feature engineering to model deployment on cloud platforms. Each project demonstrates practical application of ML algorithms to solve real-world problems.
End-to-End ML Pipeline with Web Deployment
A complete machine learning application that predicts forest fire risk based on environmental factors including temperature, humidity, wind speed, and rainfall. Built with Flask and deployed on AWS Elastic Beanstalk, this project demonstrates the full ML lifecycle from data analysis to production deployment.
Complete ML & Python Development Course
A structured learning repository covering machine learning fundamentals alongside essential Python skills. Includes hands-on implementations of regression models, data visualization, feature engineering, and web app development with Flask and Streamlit.
10+ ML Algorithm Implementations
A comprehensive collection of machine learning algorithms with practical implementations. Each algorithm is demonstrated with real datasets, covering both supervised and unsupervised learning techniques.