Heart Disease Detection

A heart disease prediction system built using classical machine learning methods. The pipeline includes robust preprocessing (handling missing values, feature scaling, categorical encoding), followed by supervised classification using Logistic Regression, Random Forest, and K-Nearest Neighbors. Achieved 85% accuracy on the Cleveland Heart Disease dataset with full model comparison and feature importance analysis.