Heart Disease Data Analysis Using Machine Learning Models

This project explores heart disease patient data using machine learning libraries. It includes data preprocessing, transformation, and training classification models such as Logistic Regression and Random Forest, along with clustering techniques like KMeans and Agglomerative Clustering. Model performance is evaluated using accuracy, classification metrics, and visual interpretation of results.