The overall objective of our project is to predict accurately with less number of tests and attributes the presence of heart disease. In this project, fourteen attributes are considered which form the primary basis for tests and give accurate results. Five data mining classification techniques were applied namely K-Nearest Neighbor, Naive Bayes, Decision Tree, Random Forest & Logistic Regression. It is shown that Random Forest has better accuracy than the other techniques.