Heart Disease Prediction App

This application predicts the likelihood of heart disease based on user-provided medical attributes. It leverages machine learning models from scikit-learn and provides a user-friendly interface using Streamlit. The app is deployed on the web.

Features

• Predicts the probability of heart disease using a machine learning model.
• User-friendly interface built with Streamlit.
• Web-deployed application for accessibility.

Accuracy

The machine learning model used in this app achieves an accuracy of 86% on the dataset it was trained on. Performance metrics such as precision, recall, and F1-score may vary based on user input and dataset characteristics.

Usage

To use the application locally, follow these steps:

1. Clone the repository: git clone https://github.com/an-admin/Heart_dis.git

   cd heart-disease-prediction-app

2. Install dependencies: pip install -r requirements.txt

3. Run the app: streamlit run app.py

4. Access the app: Open your web browser and go to http://localhost:8501 to view and interact with the application.

Deployment

The app is deployed using Streamlit Sharing. Visit (https://heartdispredict.streamlit.app/) to use it online.

Contributing

Contributions are welcome! If you want to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -am 'Add new feature').
5. Push to the branch (git push origin feature-branch).
6. Create a new Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.