This repository implements a data-driven student monitoring system that tracks academic performance and provides predictive insights. By analyzing grades, attendance, and behavioral patterns, the system helps educators identify at-risk students early and make informed decisions to improve learning outcomes.
Designed for scalability and real-time analysis, this system integrates machine learning models with interactive dashboards to support data-driven education.
- 🧠 Predictive Analytics for Student Performance
- 📊 Early Identification of At-Risk Students
- ⚡ Real-Time Data Tracking & Visualization
- 🔍 Explainable AI Insights for Educators
- 🌐 Scalable Architecture for Schools & Universities
- Python 3.10+
- Scikit-learn → Random Forest, Gradient Boosting
- XGBoost / LightGBM → High-performance prediction
- TensorFlow / Keras → Neural networks
- Pandas & NumPy → Data processing
- Matplotlib / Seaborn → Visualization
- SHAP → Feature contributions & interpretability
- Streamlit / Dash → Interactive dashboards
- FastAPI / Flask → REST API deployment
- Docker → Containerization
├── data/
│ ├── raw/
│ ├── processed/
│
├── models/
│ ├── risk_prediction/
│ ├── performance_prediction/
│
├── notebooks/
│ ├── data_exploration.ipynb
│ ├── model_training.ipynb
│
├── src/
│ ├── data_preprocessing.py
│ ├── feature_engineering.py
│ ├── ml_models.py
│ ├── neural_models.py
│ ├── shap_explainability.py
│ ├── api.py
│
├── dashboard/
│ ├── app.py
│
├── results/
│ ├── metrics/
│ ├── visualizations/
│
├── requirements.txt
├── Dockerfile
└── README.md# Clone repository
git clone https://github.com/your-username/student-monitoring-ml.git
cd student-monitoring-ml
# Create virtual environment
python -m venv venv
source venv/bin/activate # Linux/Mac
venv\Scripts\activate # Windows
# Install dependencies
pip install -r requirements.txtpython src/ml_models.py
python src/neural_models.pypython src/api.pystreamlit run dashboard/app.py| Metric | Model Performance |
|---|---|
| Accuracy | High |
| Precision | Optimized |
| Recall | Strong |
| F1-Score | High |
| ROC-AUC | Excellent |
- Early warning for underperforming students
- Academic intervention planning
- Performance trend monitoring
- Data-driven decision support for educators
- Improves student retention and success rates
- Supports data-driven educational strategies
- Provides transparent, interpretable predictions using SHAP
- Scalable for schools, colleges, and universities
- Integration with learning management systems (LMS)
- Real-time streaming analytics for continuous monitoring
- Advanced neural models (LSTM / Transformers) for sequence-based prediction
- Mobile dashboard for teachers and administrators
# Build Docker image
docker build -t student-monitoring-ml .
# Run container
docker run -p 8000:8000 student-monitoring-mlLenny Lewis AI Developer | Data Scientist | EdTech Analytics Specialist
- 🌐 GitHub: https://github.com/Lenny-Lewis
- 💼 Portfolio: Add your portfolio link here
MIT License © 2026 Lenny Lewis
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