🎓 Student Performance AI Predictor

An end-to-end Machine Learning application that predicts student academic performance based on demographic, social, and previous academic metrics. This project demonstrates full-stack ML engineering, from model integration to a modern, responsive user interface.

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✨ Key Features

• 🎯 AI Predictions: High-accuracy predictions using a Random Forest Classifier.
• 📱 Modern UI: Responsive dashboard built with a Glassmorphism design system.
• 📊 Real-time Analysis: Immediate feedback based on 13+ student metrics.
• 🔒 Secure Access: Administrative login system for dashboard access.
• 🏗️ Clean Architecture: Modularized backend with dedicated ML utilities and clean routing.

🛠️ Tech Stack

• Backend: Python, Flask
• Machine Learning: Scikit-Learn, Pandas, NumPy, Joblib
• Frontend: HTML5, CSS3 (Custom Glassmorphism System), Bootstrap 5, Jinja2
• Data Visualization: Chart.js (Planned)
• Environment: Virtualenv, Docker (Planned)

📐 Architecture

The project follows a modular structure for scalability and maintainability:

├── app.py              # Main Flask entry point
├── utils/
│   └── ml_model.py     # ML Model handler & Inference logic
├── static/
│   └── css/
│       └── style.css   # Custom Design System
├── template/           # Jinja2 HTML Templates
├── Dataset/            # UCI Student Performance Data
└── model.sav           # Trained RandomForest Serialized Model

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)

Installation

1. Clone the repository

   git clone https://github.com/yourusername/Student-Performance-Prediction.git
   cd Student-Performance-Prediction

2. Set up a virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Run the application

   python app.py

   Visit http://127.0.0.1:5000 in your browser.

📊 Model Information

The model was trained on the UCI Student Performance Dataset, which includes student achievement in secondary education of two Portuguese schools.

Input Features included:

• Demographics: Age, Sex, Address Type
• Family: Parent education level, extra educational support
• Social: Travel time, frequency of going out, internet access
• Academic: Previous failures, Period 1 & 2 Grades

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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Developed with ❤️ for Modern Education Technology.