Student Depression Prediction System

A web application that helps predict and analyze student depression levels based on various factors including academic performance, lifestyle, and mental health history.

Features

• Prediction System: Multi-step form to collect student data and predict depression levels
• Analysis Tools: Detailed analysis of factors affecting student mental health
• Modern UI: Clean and responsive design with light/dark mode support
• Interactive Navigation: Smooth scrolling and intuitive user interface

Pages

• Home: Landing page with project overview
• Predict: Multi-step form for depression prediction
• Analyze: Detailed analysis and insights
• About: Project information and team details

Technical Stack

• HTML5
• CSS3 (with CSS Variables for theming)
• JavaScript (Vanilla)
• Geist Font Family

Setup and Running

1. Clone the repository:

git clone https://github.com/GenyoNguyen/StudentDepressionPrediction
cd StudentDepressionPrediction

2. Start a local server. You can use any of these methods:

   Using Python:

   # Python 3
   python -m http.server 3000
   
   # Python 2
   python -m SimpleHTTPServer 3000

   Using Node.js:

   npx serve

3. Open your browser and navigate to:

   http://localhost:3000
   

Project Structure

.
├── index.html          # Home page
├── predict.html        # Prediction form
├── analyze.html        # Analysis page
├── about.html          # About page
├── backend/           # Backend API (see backend/README.md for details)
├── css/
│   └── styles.css      # Main stylesheet
├── js/
│   └── script.js       # JavaScript functionality
├── static/
│   └── assets/         # Icons and images
└── fonts/             # Custom fonts

For detailed information about the backend API, please refer to backend/README.md.

Development

• The project uses CSS variables for theming and easy customization
• JavaScript handles form validation and theme switching
• All pages are responsive and work on mobile devices
• The prediction form uses a multi-step approach for better user experience

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.