Heart Disease Prediction App

A simple Streamlit web app that uses a trained logistic regression model to estimate heart disease risk from a small set of clinical inputs.

Overview

The app loads a pre-trained model, scaler, and expected feature list from the repository and turns user inputs into a prediction in the browser. It is designed to be lightweight and easy to deploy on GitHub and Streamlit Community Cloud.

Features

• Interactive Streamlit form for entering patient details
• One-click prediction with risk feedback
• Uses the bundled model artifacts in the repo
• Minimal setup for local development and deployment

Tech Stack

• Python
• Streamlit
• Pandas
• Joblib
• Scikit-learn

Project Files

• app.py - main Streamlit application
• LogisticRegressionHeart.pkl - trained classification model
• scaler.pkl - fitted feature scaler
• columns.pkl - expected model input columns

Local Setup

1. Create and activate a virtual environment.
2. Install the required packages:

pip install streamlit pandas joblib scikit-learn

3. Run the app:

streamlit run app.py

Important Note

This app provides an ML-based estimate and is not a medical diagnosis tool. It should not be used as a substitute for professional medical advice.