This web application is designed to predict the likelihood of heart disease based on user input. It utilizes a machine learning model trained on a dataset of historical heart health records. Users can input various health parameters, and the app will provide a prediction along with relevant insights.
Deployed link: https://healthpredictt.streamlit.app/
- User-friendly Interface: Easy-to-use web interface for entering health parameters.
- Prediction Results: Instant prediction of the likelihood of heart disease.
- Interpretability: Explanation of the factors contributing to the prediction.
- Data Security: Ensure the privacy and security of user input.
Follow these steps to set up the project locally:
- Clone the repository:
git clone https://github.com/your-username/project-1.git - Navigate to the project directory:
cd <req directory> - Install dependencies:
pip install -r requirements.txt - Run the web app:
python app.py
- Open the web app in your browser.
- Fill in the required health parameters.
- Click the "Predict" button.
- View the prediction results and insights.
- Frontend: HTML, CSS, JavaScript
- Backend: Flask (Python)
- Machine Learning: scikit-learn, TensorFlow, or PyTorch (customize based on your model)
- Deployment: streamlit