Heart Disease Detection

A heart disease prediction pipeline built on the Cleveland Heart Disease dataset. The project covers the full supervised learning workflow: data cleaning, feature scaling, categorical encoding, then benchmarking three classifiers: Logistic Regression, Random Forest, and K-Nearest Neighbors, using cross-validation. The best model reached 85% accuracy. SHAP values were used to interpret which features drove predictions, making the model's behavior transparent rather than just measuring its performance.