Heart diseases are a major killer in India and throughout the world, application of promising technology like machine learning to the initial prediction of heart diseases willhave a profound impact on society. The early prognosis of heart disease can aid in making decisions on lifestyle changes in high-risk patients and in turn reduce the complications, which can be a great milestone in the field of medicine. The number of people facing heart diseases is on a raise each year. This prompts for its early diagnosis and treatment. The utilization of suitable technology support in this regard can prove to be highly beneficial to the medical fraternity and patients. In this paper, the different machine learningalgorithms used to measure the performance are KNN,SVM, Decision Tree, Random Forest, Logistic Regression, and Extreme Gradient Boosting applied on the dataset. The expected attributes leading to heart disease in patients are available in the dataset which contains14 important features that are useful to evaluate the system are selected among them. To increase efficiency, attribute selection is done. In this n features have to be selected for evaluating the model which gives more accuracy Comparing all seven the KNN, Random Forest, extreme gradient boosting classifier gives the highest accuracy of 96.75%.