Diabetes Prediction Web Application

A Django-based web application that predicts diabetes using machine learning. The application provides user authentication and a simple interface for users to input their health metrics and receive predictions.

Features

• User Authentication (Register, Login, Logout)
• Diabetes Prediction using Machine Learning
• Responsive Web Interface
• About and Contact Pages
• Form-based Data Input

Tech Stack

• Python 3.x
• Django
• Scikit-learn (Machine Learning Model)
• SQLite Database
• HTML/CSS
• Bootstrap (for styling)

Project Structure

secondproject/
├── app2/                   # Main application directory
│   ├── migrations/        # Database migrations
│   ├── templates/        # HTML templates
│   ├── forms.py         # User registration forms
│   ├── models.py        # Database models
│   ├── urls.py          # URL configurations
│   ├── views.py         # View functions
│   └── training.py      # ML model training script
├── static/               # Static files (CSS, JS, Images)
├── templates/            # Global templates
├── manage.py            # Django management script
├── model.joblinb        # Trained ML model
└── db.sqlite3           # SQLite database

Installation

1. Clone the repository:

git clone <repository-url>

2. Create a virtual environment and activate it:

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

3. Install required packages:

pip install django scikit-learn joblib pandas numpy

4. Run migrations:

python manage.py migrate

5. Start the development server:

python manage.py runserver

Usage

1. Register a new account or login with existing credentials
2. Navigate to the prediction form
3. Enter the required health metrics:
   • Glucose Level
   • Blood Pressure
   • Skin Thickness
   • Insulin
   • BMI (Body Mass Index)
   • Diabetes Pedigree Function
   • Age
4. Submit the form to get the prediction result

Contributing

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

License

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

Contact

Your Name - your.email@example.com Project Link: https://github.com/yourusername/secondproject