Predictive Modeling for Cleveland Heart Disease Diagnosis

Utilized machine learning algorithms, including Neural Network, CART, and Random Forest, to analyze the Cleveland heart disease dataset. Compared the performance of the models based on metrics such as mean squared error, accuracy, and error rate. Found that Random Forest achieved the highest accuracy (83.14607%), followed by the Neural Network (77.72006%), and CART model.