🎓 Student Performance Prediction System (Advanced ML Pipeline)

This is an industry-oriented machine learning project designed to predict student performance levels using academic and behavioral signals. It features a modular Python architecture, an automated XGBoost pipeline, and a FastAPI inference service.

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📊 Project Visuals

1️⃣ Key Risk Factors

Analysis of academic features impacting student performance predictions.

2️⃣ Risk Distribution

Breakdown of overall student risk status (Safe vs At-Risk).

🚀 Key Features

• 🎯 Advanced XGBoost Model: Optimized for high-accuracy classification and early risk detection.
• 📊 Automated Visuals: Real-time generation of feature importance and distribution plots.
• 🌐 FastAPI Integration: Production-ready REST API for real-time inference.
• 🏗️ Modular Architecture: Clean separation of data preprocessing, model development, and serving.
• 📱 Interactive Dashboard: Auto-generated HTML dashboard for easy analysis.

🛠️ Tech Stack

Category	Tools
Machine Learning	XGBoost, Scikit-learn, Pandas, Numpy
Backend/API	FastAPI, Uvicorn, Pydantic
Visualization	Plotly, Kaleido
DevOps/Tools	Git, Joblib, Virtualenv

📂 Project Structure

├── data/           # Simulated datasets (CSV)
├── src/            # Preprocessing & Model Development Logic
├── models/         # Saved XGBoost model and Scaler artifacts (.pkl)
├── images/         # Auto-generated visualization plots (.png)
├── outputs/        # Performance metrics and HTML dashboard
├── main.py         # Main Pipeline Orchestrator & API Entry
├── requirements.txt # Project dependencies
└── README.md       # Project documentation
⚙️ How to Run
1. Setup Environment
Bash
# Clone the repository
git clone [https://github.com/dalimkumar452-sudo/Student-Performance-Prediction.git](https://github.com/dalimkumar452-sudo/Student-Performance-Prediction.git)

# Navigate to directory
cd Student-Performance-Prediction

# Install dependencies
pip install -r requirements.txt
2. Execute Pipeline
Bash
python main.py
Note: This will train the model, generate images, save the dashboard, and start the API server.

3. Test API
Open your browser and go to:

Swagger UI: http://127.0.0.1:8000/docs

Home: http://127.0.0.1:8000/

👨‍💻 Developed by
Dalim Kumar Machine Learning Enthusiast & Developer GitHub Profile

Disclaimer: This project is part of an academic coursework for scientific research and predictive modeling