Student Result Analysis System

A comprehensive Flask-based web application for scraping and analyzing student academic results with advanced data visualization and customizable Excel exports.

Features

• Web Scraping: Automatically scrape student results from university portals
• Dynamic Semester Detection: Automatically detects and handles any number of semester tables (sem1, sem2, sem3, sem4, etc.)
• Column Selection: Choose which data fields to include in Excel exports via checkbox interface
• Data Analysis: Comprehensive statistical analysis with interactive charts
• Professional UI
• Excel Export: Customizable Excel reports with enhanced formatting

Prerequisites

• Python 3.8 or higher
• Chrome browser (for web scraping)
• Git

Installation

1. Clone the repository

   git clone <repository-url>
   cd student-result-analysis

2. Create virtual environment

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

3. Install dependencies

   pip install -r requirements.txt

4. Set environment variables

   # Create .env file
   echo "SESSION_SECRET=your-secret-key-here" > .env
   
   # On Windows Command Prompt
   set SESSION_SECRET=your-secret-key-here
   
   # On Windows PowerShell
   $env:SESSION_SECRET="your-secret-key-here"
   
   # On macOS/Linux
   export SESSION_SECRET="your-secret-key-here"

Running the Application

1. Start the Flask application

   python app.py

   Or using Gunicorn (recommended for production):

   gunicorn --bind 0.0.0.0:5000 --reuse-port --reload main:app

2. Access the application Open your browser and navigate to: http://localhost:5000

Usage

Web Scraping

1. Choose between individual roll numbers or range-based scraping
2. Select which columns to include in the Excel export
3. Enter roll numbers or specify a range
4. Click "Start Scraping" to begin the process
5. Monitor progress in real-time
6. Download the generated Excel file

Data Analysis

1. Upload an existing Excel or CSV file
2. View comprehensive statistical analysis
3. Explore interactive charts and visualizations
4. Download enhanced analysis reports

Project Structure

student-result-analysis/
├── app.py                 # Main Flask application
├── main.py               # Application entry point
├── scraper.py            # Web scraping functionality
├── data_analyzer.py      # Data analysis engine
├── requirements.txt      # Python dependencies
├── static/
│   ├── css/
│   │   └── style.css    # Custom styling
│   └── js/
│       ├── main.js      # Main JavaScript
│       └── charts.js    # Chart functionality
├── templates/
│   ├── base.html        # Base template
│   ├── index.html       # Main dashboard
│   ├── analyze.html     # Analysis page
│   └── progress.html    # Progress tracking
└── uploads/             # File upload directory

Configuration

Environment Variables

• SESSION_SECRET: Secret key for Flask sessions (required)
• DATABASE_URL: Database connection string (optional)

Customization

• Modify scraper.py to adapt to different university portals
• Update CSS variables in static/css/style.css for theme customization
• Adjust analysis parameters in data_analyzer.py

Dependencies

The application requires the following Python packages:

• Flask (web framework)
• Selenium (web scraping)
• Pandas (data manipulation)
• XlsxWriter (Excel generation)
• Matplotlib/Seaborn (data visualization)
• WebDriver Manager (Chrome driver management)

Browser Requirements

For web scraping functionality:

• Chrome browser must be installed
• ChromeDriver is automatically managed by webdriver-manager

Troubleshooting

Common Issues

1. ChromeDriver not found

   • Ensure Chrome browser is installed
   • Check internet connection for automatic driver download

2. Permission errors

   • Run with appropriate permissions
   • Check file system permissions for uploads/ directory

3. Port already in use

   • Change port in app.py or kill existing process
   • Use different port: python app.py --port 5001

4. Module not found errors

   • Ensure virtual environment is activated
   • Reinstall requirements: pip install -r requirements.txt

Development

Adding New Features

1. Fork the repository
2. Create a feature branch
3. Implement changes with tests
4. Submit a pull request

Code Style

• Follow PEP 8 guidelines
• Use meaningful variable names
• Add docstrings to functions
• Include error handling

License

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

Support

For issues and questions:

1. Check the troubleshooting section
2. Review existing issues
3. Create a new issue with detailed description

Contributing

Contributions are welcome! Please read the contributing guidelines before submitting pull requests.