Developed a machine learning-based heart disease prediction model and deployed it as a web application. This project aimed to leverage data science techniques to assist in early detection and risk assessment of heart disease. Key Accomplishments: - Implemented and compared multiple machine learning algorithms including Logistic Regression, Random Forest, Gradient Boosting, and SVM - Achieved 0.852459 test accuracy with the best performing model (Logistic regression) - Developed an interactive web application using Streamlit for real-time predictions - Utilized data preprocessing techniques and implemented a robust model training pipeline - Applied data visualization techniques to analyze model performance and dataset characteristics Technologies Used: Python, scikit-learn, pandas, numpy, Streamlit, matplotlib, seaborn GitHub: https://github.com/Divyansh0108/heart-disease-pred This project demonstrates proficiency in machine learning, data preprocessing, model evaluation, and web application development, showcasing the potential of AI in healthcare applications.