Heart disease predictions using machine learning

This project focuses on predicting heart disease using machine-learning algorithms trained on medical data. It analyzes key health indicators such as age, blood pressure, cholesterol, and ECG results to assess heart disease risk. Multiple models including Logistic Regression, Decision Tree, Random Forest, SVM, and GridRF were trained and evaluated.

A Streamlit-based web application was developed to allow users to make single and bulk predictions easily.

The system aims to assist in early diagnosis by providing fast, data-driven insights for better healthcare decisions

Built With

• machine-learning
• python