🚀 Student Performance Indicator (End-to-End ML Pipeline)

A complete Machine Learning project that predicts student performance based on various parameters. This repository demonstrates the full lifecycle of an ML application, from data analysis to cloud deployment using Docker and CI/CD pipelines.

🛠️ Tech Stack

• Language: Python (3.8+)
• Framework: FastApi (Backend) / Scikit-Learn (ML)
• Containerization: Docker
• Cloud Provider: AWS (EC2, ECR)
• CI/CD: GitHub Actions (Self-Hosted Runner)

📂 Project Structure

├── artifacts/          # Generated models (model.pkl) & datasets (train/test.csv)
├── logs/               # Application execution logs
├── notebook/           # Jupyter notebooks for EDA & model training
├── src/                # Core application source code
│   ├── components/     # Data ingestion, transformation, & training modules
│   ├── pipeline/       # Training & Prediction pipelines
│   ├── logger.py       # Custom logging setup
│   └── exception.py    # Custom exception handling
├── static/             # CSS and styling files
├── templates/          # HTML templates for the UI
├── app.py              # Application entry point (FastAPI)
├── Dockerfile          # Docker configuration
├── requirements.txt    # Python dependencies
└── setup.py            # Project packaging setup

📊 Features

• Data Ingestion & Transformation: Automated pipelines to handle data processing.
• Model Training & Prediction pipeline: Regression models trained to predict student scores.
• Web Interface: Web UI for user inputs (built with FastAPI/Jinja2).
• Automated Deployment: Fully automated CI/CD pipeline triggering on every push.

⚙️ How to Run Locally

1. Clone the Repository

git clone [https://github.com/Arpit2744/ML-Project.git](https://github.com/Arpit2744/ML-Project.git)
cd ML-Project

2. Create a Virtual Environment

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

3. Install Dependencies

pip install -r requirements.txt

4. Run the App

python app.py

Open http://127.0.0.1:8000 in your browser.

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🐳 Docker Support

Build and run the container locally to ensure consistency.

docker build -t student-performance .
docker run -p 8000:8000 student-performance

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☁️ Deployment Pipeline (CI/CD)

This project uses GitHub Actions to automate deployment to AWS EC2.

Workflow Steps:

1. Integration: Code is linted and tested.
2. Delivery: Docker image is built and pushed to AWS ECR (Elastic Container Registry).
3. Deployment: A Self-Hosted Runner on an AWS EC2 instance pulls the latest image and updates the running container.

🚧 Challenges & Learnings

Building this wasn't just about coding; it was about engineering. Here are the real-world issues I solved:

• EC2 Disk Space: The 1.6GB Docker image crashed the t2.micro instance. I learned to resize AWS EBS volumes and expand Linux partitions on the fly.
• Git Divergence: Solved non-fast-forward merge conflicts when syncing local code with remote repo changes.
• Docker Zombie Containers: Automated the cleanup of old containers in the CI/CD pipeline to prevent name conflicts during re-deployment.
• Missing Artifacts: Debugged FileNotFoundError caused by .gitignore excluding the trained model file (model.pkl) from the build.

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📜 License

This project is open-source and available under the MIT License.