Among whole Human body, Heart plays a vital role where it pumps or circulates the blood to all parts of the body. As a result, whole body will function in a proper condition. Heart disease is a major threat globally which takes a human life easily if it is not treated at the proper condition sometimes it can even lead to death. Basically, if the pumping of blood throughout the body is not in proper condition heart disease surely arises. Many risk factors exist which actually stops pumping the blood, those factors are used to get the accurate and early detection of the disease. Researcher generally use data mining and machine learning approaches for dealing large amount of data. For heart or cardiac prediction model that is to handle huge amount of medical data Machine learning approaches can used to produce higher accuracy. In the proposed method three machine Learning approaches are included for predicting and comparing the heart disease. The Models include Logistic Regression, K-Nearest Neighbor and finally Random Forest algorithms are compared for the accuracy and to find the best model which is having greater accuracy.