Millions of deaths occur worldwide because of heart diseases per annum. Prediction and diagnosis of the
diseases related to the heart require more accuracy, precision, and faultlessness, as the slightest mistake can
cause various problems like fatigue and even result in the death of the person. In our project, we predict the
chance of having heart disease by checking the accuracy of machine learning algorithms. For this the algorithms
are Logistic Regression, K-Nearest Neighbor (KNN), Random Forest by using the UCI repository dataset for
training and testing. Our aim is to seek out an appropriate machine learning technique that’s efficient likewise
as accurate for the prediction of heart condition.
Software And Hardware