The heart plays an important role in the human body. The diagnosis and prediction of heart-related diseases require greater precision, perfection, and correctness because a minor error can result in exhaustion or death. There are numerous death cases related to the heart, and the number is growing exponentially day by day. This task presents several machine-learning techniques for predicting heart diseases using data on major health figures from patients. The paper shows four classification methods: k-nearest neighbor, support vector machine (SVM), random forest (RF), and linear regression (LR) to build the prediction models. After that, we compare our algorithm with Multilayer Perceptron (MLP), Decision Tree (DT), Random Forest (RF), and Naive Bayes (NB) to build the prediction models. Data pre-processing and feature selection steps were done before building the models. The models were evaluated based on accuracy, precision, recall, and F1 score. The SVM model performed best with 93.17% accuracy. For the implementation, we have used Python programming, which has many types of libraries and header files that make the work more accurate and precise.

MLP, SVM, Pre-processing, RF, Linear Regression.