This project focused on predicting heart disease risk through data preprocessing, exploratory analysis, and modeling. Data was cleaned, transformed, and enriched with derived features. Regression quantified relationships, while classification categorized patients into risk groups. Models were evaluated on metrics like accuracy and sensitivity, showcasing the complementary strengths of both approaches for effective heart disease prediction.