A machine learning-based predictive system designed to identify the likelihood of heart disease using patient health data. The project applies data preprocessing, exploratory data analysis (EDA), and multiple machine learning algorithms to analyze medical features and predict the risk of heart disease.
The model is trained on clinical attributes such as age, cholesterol level, blood pressure, heart rate, and other relevant health indicators to assist in early detection and decision support. This project demonstrates the complete machine learning pipeline including data cleaning, feature selection, model training, evaluation, and performance comparison.
Technologies Used: Python, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn