In this project, we will apply basic machine learning methods to predict whether a person is likely to develop heart disease based on the Cleveland Heart Disease dataset from the UCI Machine Learning Repository.

The Cleveland dataset comprises 14 features including age, gender, chest pain type, resting blood pressure, serum cholesterol level, fasting blood sugar, resting ECG result, maximum heart rate achieved, exercise-induced angina, ST depression induced by exercise relative to rest, peak exercise ST segment, number of major vessels (0-3) colored by fluoroscopy, thalassemia status, and heart disease diagnosis (0 representing no disease and 1, 2, 3, 4 representing varying degrees of disease). The Cleveland dataset consists of 303 samples with 14 features.