Student Performance Prediction System

Advanced Machine Learning for Student Success Prediction

📚 Project Overview

This comprehensive student performance prediction system is a multifaceted machine learning project designed to help educational institutions proactively identify at-risk students and improve academic outcomes. The system includes three specialized prediction models that address different aspects of student performance:

1. Final Exam Mark Predictor: Predicts a student's final examination marks based on attendance, coursework, and internal assessments
2. Dropout & Academic Success Predictor: Identifies students at risk of dropping out with high accuracy (97%)
3. Exam Performance Pass/Fail Predictor: Forecasts whether a student will pass or fail upcoming exams

Note: This project began as a university assignment but has evolved into a personal ongoing project. It is currently under active development with regular improvements and bug fixes.

🧠 Machine Learning Models

1. Student Final Exam Mark Predictor

• Type: Random Forest Regressor
• Features:
  • Attendance records (present/absent counts)
  • Lecture test marks
  • Lab test marks
  • Mid-semester evaluation marks
  • Pre-GTU marks
  • Internal assessment marks
• Performance:
  • Low Mean Absolute Error (MAE)
  • Low Root Mean Squared Error (RMSE)
  • Feature importance visualization
  • SHAP explainability for transparent predictions

2. Student Dropout & Academic Success Predictor

• Type: Random Forest Classifier with hyperparameter tuning via RandomizedSearchCV
• Features:
  • Demographic information
  • Previous academic performance
  • Engagement metrics
  • Socioeconomic indicators
• Performance:
  • Accuracy: 97%
  • Comprehensive classification report
  • Confusion matrix visualization
  • ROC curve analysis
  • Cross-validation for robust evaluation

3. Student Exam Performance Pass/Fail Predictor

• Type: Logistic Regression with Pipeline (StandardScaler + Normalizer)
• Features:
  • Study hours
  • Previous exam scores
• Performance:
  • Accuracy: 88%
  • SMOTE handling for class imbalance
  • Cross-validation scoring
  • Intuitive prediction visualization

🌟 Key Features

• Interactive Web Interface: Multi-page web application with step-by-step forms
• Multi-step Prediction Forms: User-friendly data collection
• Dark Mode Support: Toggle between light and dark themes
• Explainable AI: SHAP values help interpret prediction factors
• Data Visualization: Comprehensive visual representations of model performance
• Batch Processing: Upload CSV files to process multiple students at once
• Error Handling: Robust error management throughout
• Responsive Design: Works well across different device sizes

🛠️ Technical Implementation

Backend

• Framework: Flask web server
• Machine Learning: scikit-learn, SHAP for explainability
• Data Processing: pandas, numpy for data manipulation
• Model Persistence: Serialized models via joblib/pickle
• Validation: Cross-validation and testing methodology

Frontend

• Structure: HTML5 for content structure
• Styling: CSS3 with responsive design
• Interaction: JavaScript for dynamic behavior
• Animation: CSS transitions and animations
• User Experience: Multi-step forms with validation

📊 Model Results

Final Exam Mark Predictor

• Mean Absolute Error: < 5 marks
• Feature importance shows attendance and mid-term scores as key predictors

Dropout Risk Predictor

• High precision in identifying at-risk students
• Low false positive rate to prevent unnecessary interventions
• Feature importance highlights key early warning signs

Pass/Fail Predictor

• 88% accuracy in predicting exam outcomes
• Balanced precision and recall metrics

🔍 Use Cases

1. Early Intervention: Identify at-risk students before academic failure
2. Resource Allocation: Focus additional support on students who need it most
3. Student Self-Assessment: Allow students to understand factors affecting their performance
4. Academic Planning: Help advisors make data-driven recommendations
5. Institutional Planning: Improve overall retention and success rates

📱 Screenshots

(Screenshots coming soon - UI/UX improvements in progress)

⚠️ Current Status and Known Issues

This project is currently in active development. While the core functionality works, there are some known issues:

• Some UI elements need refinement for better mobile responsiveness
• Occasional prediction inconsistencies with certain edge case inputs
• CSV batch upload functionality has validation limitations
• Model retraining pipeline needs improvement for new data incorporation
• Some browser compatibility issues in Internet Explorer and older browsers

I am actively working on resolving these issues and implementing new features. Bug reports, feature requests, and contributions are welcome!

🚀 Roadmap and Future Enhancements

• User authentication and student profiles
• Mobile application version
• Additional predictive models for other academic metrics
• Integration with institutional LMS platforms
• Long-term tracking and performance analytics
• Recommendation engine for improvement strategies
• Enhanced data visualization dashboard
• API endpoints for third-party integration
• Multi-language support
• Automated model retraining with new data
• Unit and integration test coverage

🔧 Project Structure

project_student_performance/
├── Project-Predict-Student-dropout-and-academic-success/  # Dropout prediction model
│   ├── app.py                 # Flask application
│   ├── Trainingmodel.py       # Model training script
│   ├── static/                # Frontend assets
│   └── templates/             # HTML templates
├── Project-Student-Exam-Performance-Prediction/      # Pass/fail prediction
│   ├── app4.py                # Flask application
│   ├── Trainingmodel1.py      # Model training script
│   ├── static/                # Frontend assets
│   └── templates/             # HTML templates
├── project-Student-Performace/                  # Final exam mark predictor
│   ├── app.py                 # Flask application
│   ├── Trainigmodel4.py       # Model training script
│   ├── static/                # Frontend assets
│   └── templates/             # HTML templates
└── Project-Run/               # Integrated web application
    ├── Frontend-main-Rashed/  # Combined frontend
    │   ├── Main/              # Main application
    │   │   ├── Run.py         # Main application entry point
    │   │   ├── Static/        # Static assets
    │   │   └── templates/     # HTML templates
    │   └── project folders    # Individual project components
    └── ...

🧪 Technologies Used

• Python: Core programming language
• Flask: Web framework
• scikit-learn: Machine learning library
• pandas/numpy: Data manipulation libraries
• SHAP: Model explainability
• Matplotlib/Seaborn: Data visualization
• joblib/pickle: Model serialization
• HTML/CSS/JavaScript: Frontend development

🤝 Contributing

Contributions to this project are welcome and appreciated! If you'd like to contribute:

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

I'm more than happy to review and merge valuable contributions. Whether it's bug fixes, feature enhancements, or documentation improvements - all help is welcome!

📄 License

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

📬 Contact

If you have any questions, suggestions, or would like to collaborate, please feel free to reach out.

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This project originated as a university assignment and has evolved into a personal ongoing project. I apologize for any bugs or issues you may encounter - the system is continuously being improved. Thank you for your interest and understanding!