Heart Disease Prediction using Machine Learning

Developed a comprehensive machine learning pipeline to predict the probability of heart disease using patient health metrics.

Preprocessed and cleaned the dataset for consistency and accuracy

Applied feature selection and dimensionality reduction (PCA)

Trained and compared supervised and unsupervised ML algorithms

Performed hyperparameter tuning for optimized results

Built an interactive Streamlit web app for real-time predictions

This project demonstrates skills in data preprocessing, model building, evaluation, and deployment.