🧠 Student Performance Indicator (SPI)

An end-to-end Machine Learning project showcasing a complete ML pipeline from data preprocessing to deployment using Docker and Microsoft Azure.

🌐 Live Demo

Click here to open the deployed app

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📌 Overview

This project predicts student performance based on academic and demographic features. It's built to demonstrate:

• Clean machine learning pipeline with modular design
• Deployment-ready Flask API
• Docker containerization
• CI/CD using GitHub Actions
• Live deployment on Azure Web App via DockerHub

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🧰 Tech Stack Used

Python, Flask, scikit-learn, XGBoost, CatBoost, Docker, GitHub Actions, Microsoft Azure

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⚙️ How It Works

1. Preprocessing: Clean and encode raw data using pandas, scikit-learn
2. Model Training: Train using ML algorithms like CatBoost & XGBoost
3. API Development: Serve model predictions through a Flask API
4. Dockerization: Containerized using a Dockerfile for easy deployment
5. CI/CD: GitHub Actions automates Docker builds and Azure deployment
6. Deployment: Hosted on Azure Web App (F1 Free Tier)

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💻 Running Locally

# Clone the repository
git clone https://github.com/yourusername/SPI.git
cd SPI

# Build Docker image
docker build -t spi:latest .

# Run the container
docker run -p 5000:5000 spi:latest

Now visit: http://localhost:5000

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