Student Performance Predictor 🤖📊

AI-powered system for predicting student academic performance and identifying at-risk students

🎯 Problem Solved

This project addresses a critical challenge in modern education: predicting student performance to enable early intervention and personalized support. Using machine learning, it analyzes various factors affecting academic success and provides actionable insights for educators.

Key Benefits:

• 🔍 Early Warning System: Identify at-risk students before they fail
• 📈 Performance Analytics: Understand factors influencing academic success
• 🎯 Personalized Recommendations: Data-driven insights for student support
• 📊 Automated Reporting: Daily analysis with visual dashboards

🚀 Quick Start

Installation

Basic Usage

Python API

📊 Example Results

Model Performance Comparison

Feature Importance

The analysis reveals that the most important factors for student performance are:

1. Study Time (Importance: 0.324)
2. Previous Grade (Importance: 0.287)
3. Absences (Importance: 0.198)
4. Parental Support (Importance: 0.124)
5. Age (Importance: 0.067)

📁 Project Structure

student-performance-predictor/
├── src/                          # Source code
│   ├── __init__.py
│   ├── main.py                   # CLI entry point
│   ├── data_processor.py         # Data preprocessing
│   ├── predictor.py              # ML models and prediction
│   └── visualizer.py             # Visualization tools
├── tests/                        # Unit tests
│   ├── test_data_processor.py
│   └── test_predictor.py
├── data/                         # Sample datasets
│   └── sample_student_data.csv
├── docs/                         # Documentation
├── scripts/                      # Utility scripts
├── .github/workflows/            # CI/CD workflows
│   ├── tests.yml                 # Main test workflow
│   └── daily_report.yml          # Scheduled report generation
├── reports/                      # Generated reports (gitignored)
├── requirements.txt              # Python dependencies
├── .gitignore                    # Git ignore rules
└── README.md                     # This file

🧪 Testing

Run the comprehensive test suite:

🔄 CI/CD Features

1. Automated Testing (

.gitverse/workflows/tests.yml

)

• ✅ Runs on every push/PR
• ✅ Multi-version Python testing (3.9, 3.10, 3.11)
• ✅ Code quality checks (flake8, black, isort)
• ✅ Unit tests with coverage
• ✅ Artifact upload for reports

2. Daily Automated Reports (

.gitverse/workflows/daily_report.yml

)

• 📅 Scheduled: Runs daily at 2 AM UTC
• 🎛️ Manual Trigger: Can be run on-demand with custom parameters
• 📦 Artifacts: Saves reports and visualizations
• 🚀 Auto-deploy: Publishes to GitHub Pages
• 📝 Auto-commit: Updates repository with latest reports

Features:

• Automatic performance analysis
• Interactive dashboards
• Model comparison charts
• Feature importance analysis
• GitHub Pages deployment

📈 Visualizations Generated

The system creates comprehensive visualizations:

1. Correlation Matrix: Shows relationships between features
2. Feature Importance: Identifies key performance drivers
3. Model Comparison: Compares different algorithms
4. Predictions vs Actual: Validates model accuracy
5. Performance Distribution: Shows score distributions
6. Interactive Dashboard: Plotly-based interactive analysis

🎓 Educational Use Cases

For Teachers:

• Identify students needing additional support
• Understand factors affecting class performance
• Generate automated progress reports

For Students:

• Get personalized performance predictions
• Understand how study habits affect grades
• Receive targeted improvement recommendations

For Administrators:

• Analyze school-wide performance trends
• Evaluate intervention program effectiveness
• Generate institutional reports

🔧 Configuration

Data Format

Your CSV should include these columns:

• age
  : Student age
• gender
  : Student gender (M/F)
• study_time
  : Weekly study hours
• absences
  : Number of absences
• parental_support
  : Level of parental support (High/Medium/Low)
• extracurricular
  : Participation in extracurricular activities (Yes/No)
• previous_grade
  : Previous semester grade
• performance_score
  : Target variable (current performance)

Customization

📊 Sample Data

The project includes

🤝 Contributing

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

📝 License

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

🙏 Acknowledgments

• Built with scikit-learn, pandas, and plotly
• Inspired by educational data mining research
• Deployed using GitHub Actions and GitHub Pages

📞 Support

For questions and support:

• Create an issue in the GitHub repository
• Check the documentation folder
• Review the examples directory

⭐ Star this repo if you find it helpful!