🎓 Student Performance Analyzer

Description

AI-powered system for predicting student academic performance based on behavioral and academic metrics. The system classifies students into performance categories and provides insights for targeted interventions.

Features

• Predictive Modeling: Uses machine learning to forecast student grades
• Feature Engineering: Extracts meaningful patterns from student data
• Automated Retraining: CI/CD pipeline for model updates
• Comprehensive Testing: Full test coverage and code quality checks
• Production Ready: Clean architecture with separation of concerns

Installation

Prerequisites

• Python 3.8+
• pip or conda

Setup

Quick Start

Project Structure

.
├── src/                        # Source code
│   ├── classifier.py           # Main prediction interface
│   ├── features.py             # Feature extraction logic
│   └── model.py                # Model training pipeline
├── tests/                      # Unit tests
│ ├── init.py
│ ├── test_features.py
│ └── test_classifier.py
├── data/                       # Sample dataset
│ └── sample_questions.csv
├── docs/                       # Documentation (optional)
├── scripts/                    # Utility scripts (optional)
├── .gitverse/workflows/        # CI/CD pipelines
│   ├── tests.yml               # Runs tests on every push
│   └── retrain.yml             # # Retrains model and uploads metrics (CI/CD)
├── requirements.txt            # Python dependencies
├── setup.cfg                   # Code quality configuration
└── .gitignore

Usage Examples

1. Training a New Model

2. Feature Engineering

3. Batch Prediction

CI/CD Automation

Automated Testing

Runs on every push and pull request

Tests Python 3.8-3.11 compatibility

Code quality checks (flake8, black)

Test coverage reporting

Smart Retraining Pipeline

Weekly retraining: Automatically retrains model every Sunday

Model comparison: Checks if new model outperforms previous

Artifact storage: Saves models and metrics

Auto-PR creation: Creates PR when model improves

Reporting: Generates performance reports as GitHub Issues

Testing

Model Performance

Typical performance metrics on the sample dataset:

Accuracy: 85-92%

F1 Score: 0.84-0.90

Feature Importance: Attendance, Assignment Scores, Study Hours

Contributing

Fork the repository

Create a feature branch

Add tests for new functionality

Ensure all tests pass

Submit a pull request

License

MIT License - see LICENSE file for details

Contact

For questions or support, please open an issue in the repository.

Author: Musorka