🎓 Student Performance Prediction & Early Warning System

🚀 A full-stack Machine Learning system that predicts student academic risk and provides early warning alerts with explainable AI.

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📌 Overview

This project helps institutions identify students at Low, Medium, or High academic risk using ML models. It provides real-time predictions, alerts, analytics, and retraining capabilities.

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✨ Features

• 🎯 Risk Prediction (Low / Medium / High)
• 🚨 Smart Alert System
• 🧠 Explainable AI (Feature Importance)
• 📊 Interactive Dashboard (Streamlit)
• ⚙️ FastAPI Backend
• 📁 Dataset Upload & Retraining
• 📜 Prediction Logs & Analytics
• 📧 Email Alerts (for high-risk students)

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🧠 Tech Stack

Category	Tools
Machine Learning	Scikit-learn, XGBoost
Backend	FastAPI
Frontend	Streamlit
Visualization	Plotly
Storage	CSV / Pickle

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🏗️ Project Structure

student_performance_system/
│
├── backend/
├── frontend/
├── model/
├── data/
├── logs/
├── requirements.txt
└── README.md

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🚀 How to Run

1️⃣ Install Dependencies

pip install -r requirements.txt

2️⃣ Train Model

python model/train_model.py

3️⃣ Run Backend

cd backend
uvicorn main:app --reload

4️⃣ Run Frontend

cd frontend
streamlit run app.py

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📊 Example Output

• Risk Level: HIGH
• Alerts: Low Attendance, Backlogs
• Feature Importance Graph

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🧠 Model Details

• Random Forest + XGBoost

• Feature Inputs:

  • Attendance
  • GPA
  • Internal Marks
  • Backlogs

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🔁 Retraining Workflow

1. Upload new dataset
2. Merge with existing data
3. Retrain model
4. Deploy updated model automatically

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📈 Future Improvements

• SHAP Explainability
• Cloud Deployment
• Mobile App Integration
• Real Student Data Integration

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👨‍💻 Author

Soham Bagul

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⭐ If you like this project, give it a star!

📸 Screenshots

🔮 Prediction Page

📊 Analytics Dashboard

📋 Logs Page