Cardiovascular diseases are a major cause of death globally. Application of Computer data processing in the Medical field has been witnessing several significant revolutions in recent days. Hospitals collect a huge amount of data on patients treated by heart diseases. But the data collected is not being used properly. This data collected can be used to predict the chance of patients being attacked by heart disease. Data Mining is the process used to extract the data. The data extracted is used to train the computer about the patterns of heart disease occurrence inpatient, and then used to predict the chance of a heart disease based on the lifestyle of a person. This project proposes a prediction model to predict whether a people have a heart disease or not and to provide an awareness or diagnosis on that. This is done by comparing the accuracies of applying rules to the individual results of Support Vector Machine, Decision Trees, Random Forest, Naive Bayes classifier and KNN classifier on the dataset taken. By this project, we can build an accurate model of predicting cardiovascular disease.