This project explores the factors influencing the risk of heart attacks using real-world health data. The analysis involves comprehensive data cleaning, visualization, and model building to identify key predictors of heart disease. Various machine learning techniques such as Logistic Regression, K-Nearest Neighbors (KNN), and Clustering were applied and compared based on performance metrics and interpretability. The report provides insights into how attributes like age, cholesterol, blood pressure, chest pain type, and exercise patterns contribute to cardiovascular risk. The goal is to support early detection and clinical decision-making through data-driven analysis.

Danna Cecilia Caldas Laura Valentina Manyoma