🎓 Student Performance Prediction System

École Supérieure de Technologie - Oujda | PFE 2024/2025

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📋 About The Project

An intelligent platform designed to revolutionize educational monitoring. By combining an interactive dashboard with Machine Learning algorithms, this tool predicts student performance, detects at-risk students early, and provides personalized recommendations.

🌟 Key Features

• 🔮 AI Prediction: Future grade estimation & student classification (Excellence, Average, At-Risk).
• 📊 Intuitive Dashboards: Dedicated views for Admins, Teachers, and Students.
• 🚀 Performance: Modern architecture with React, Django, and optimized data loading.

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📸 Screenshots Showcase

🏫 Admin Dashboard

Global school statistics and prediction flow management.

Admin Dashboard	Prediction Flow
Overview of school stats	AI prediction logic visualization

👩‍🏫 Teacher Dashboard

Track classes, view ML analysis, and identify struggling students.

Teacher View	Machine Learning Analysis
Detailed class monitoring	Classification results & insights

🎓 Student Dashboard

Personalized progress tracking.

Personal space for students

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🧠 The AI Core

Powered by a Random Forest model trained on the UCI Student Performance Dataset.

• Accuracy: ~92% for Classification
• MAE: < 1.5 points for Grade Regression

See the ML Repository for technical details.

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🛠️ Tech Stack

Frontend	Backend	AI & Data
React 19	Django 5	Scikit-Learn
Material UI	DRF (REST API)	Pandas / NumPy
Recharts	PostgreSQL / SQLite	Jupyter

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🚀 Getting Started

Option 1: Docker (Recommended)

Run the entire stack with one command:

docker-compose up --build

• App: http://localhost:3000

Option 2: Manual Install

1. Backend

cd backend
pip install -r requirements.txt
python manage.py migrate
python manage.py runserver

2. Frontend

cd frontend
npm install
npm run dev

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Made with ❤️ at EST Oujda

Anass El Amrany • El khadir Safouane • Maryame Dani

© 2025 All Rights Reserved