Large number of patients related data is stored and maintained in the health industry. Heart disease is the most common one nowadays. The different ways of predicting it are Electrocardiogram (ECG), stress test, and Heart MRI. Here, the proposed model uses 13 parameters for the prediction of heart disease that includes heart rate, chest pain, cholesterol level, blood pressure, Age etc. The aim of this model is to predict whether heart disease is present or not using the various machine learning models such as Decision Tree, Random Forest, Logistic Regression, Naïve Bayes. We have achieved 0.3312 log loss using the Logistic Regression.