Heart Diseases are considered to be life-threatening and should be recognized at an early stage to make it less fatal. The most common disease is heart failure, and it is the most fatal of all and needs to be taken care of. There are many methods of treatments available for heart failures, and now machine learning and deep learning have also taken it a step forward. But sometimes due to unnecessary circumstances the prediction can go wrong and can be very fatal. To avoid that thing the authors have taken the dataset which consist of 13 main attributes/features used to predict the failure and the models which have been used to predict it are support vector machine, decision tree, knn, random forest classifier and Logistic Regression. The paper aims to provide the best model out of all the classification models with the help of the final accuracy.