Decision Trees & Random Forest Classification on Heart Disease Dataset

Supervised Learning: Classification (Decision Trees & Random Forest)

Learning Objectives

Understand how Decision Tree and Random Forest algorithms work.

Apply both models to a real medical dataset (Heart Disease Prediction).

Evaluate and interpret model performance.

Heart Disease Dataset Overview

Below is the dataset we will use in this example. It contains patient medical attributes (e.g., age, cholesterol, blood pressure)

and a target variable indicating whether the patient has heart disease.