A machine learning web application that predicts diabetes risk based on user-input health metrics. Built with Django and scikit-learn, this application provides real-time predictions using a trained logistic regression model.

• User-friendly web interface for inputting health metrics
• Real-time diabetes risk prediction
• Supports various health indicators including:
  • Age
  • Gender
  • Hypertension status
  • Heart disease history
  • Smoking history
  • BMI
  • HbA1c level
  • Blood glucose level

• Python 3.x
• Django
• scikit-learn
• pandas
• numpy
• HTML/CSS

1. Clone the repository

git clone https://github.com/yourusername/diabetes-prediction.git
cd diabetes-prediction

2. Create and activate virtual environment

python -m venv venv
source venv/bin/activate  # For Windows: venv\Scripts\activate

3. Install required packages

pip install -r requirements.txt

4. Run migrations

python manage.py migrate

5. Start the development server

python manage.py runserver

6. Visit http://127.0.0.1:8000/ in your web browser

1. Navigate to the home page
2. Fill in all required health metrics in the form
3. Click the "Predict" button
4. View your prediction result

The prediction model is a logistic regression classifier trained on diabetes-related health metrics. The model takes into account various factors such as BMI, age, blood glucose levels, and other health indicators to predict diabetes risk.

diabetes-prediction/
│
├── ml_app/                  # Main application directory
│   ├── static/             # Static files (CSS, models)
│   │   └── model/         # Trained model files
│   ├── templates/         # HTML templates
│   ├── views.py           # View functions
│   └── models.py          # Django models
│
├── manage.py              # Django management script
├── requirements.txt       # Project dependencies
└── README.md             # Project documentation

To contribute to this project:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

See requirements.txt for a list of all dependencies.

• Add data visualizations for prediction insights
• Implement user authentication
• Add ability to save prediction history
• Include more detailed health metrics

• Dataset source: [(https://www.kaggle.com/datasets/iammustafatz/diabetes-prediction-dataset)]