Heart Disease Prediction

This project is a machine learning system designed to predict the likelihood of heart disease based on patient medical data. The system analyzes multiple health indicators such as age, cholesterol level, blood pressure, maximum heart rate, and other clinical features to identify patterns associated with heart disease.

The project includes a complete machine learning workflow starting from data preprocessing and exploratory data analysis to model training and deployment.

A trained machine learning model was integrated into an interactive web application built with Streamlit, allowing users to input patient health information and receive a prediction about the risk of heart disease.

Key Features

• Data preprocessing and feature engineering

• Exploratory Data Analysis (EDA) with multiple visualizations

• Machine learning model training and evaluation

• Interactive user interface for entering patient data

• Real-time prediction of heart disease likelihood

Technologies Used

Python

Pandas

NumPy

Scikit-learn

Matplotlib

Seaborn

joblib

Streamlit