Student Performance Predictor

A machine learning web application built with FastAPI, Scikit-Learn, Jinja2, and Docker. This app predicts a student's math score based on various demographic factors and academic performance metrics.

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Project Structure

├── artifacts/
│   ├── model.pkl            # Trained ML model
│   └── preprocessor.pkl     # Fitted Scikit-Learn ColumnTransformer
├── templates/
│   └── index.html           # Jinja2 HTML template with custom CSS
├── train.py                 # Script to train model and export artifacts
├── main.py                  # FastAPI server and endpoints
├── requirements.txt         # Python dependencies
├── Dockerfile               # Docker container configuration
└── .dockerignore            # Files ignored by Docker build

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Features

• Machine Learning Pipeline: Uses ColumnTransformer with OneHotEncoder and StandardScaler paired with LinearRegression.
• FastAPI Backend: Lightweight asynchronous Python web framework handling web forms (python-multipart).
• Clean UI: Responsive, modern CSS form rendered with Jinja2 templates.
• Docker Ready: Includes a lightweight python:3.11-slim container build configuration.

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Quick Start (Local Setup)

1. Clone the Repository

git clone <your-repository-url>
cd <repository-folder-name>

2. Create and Activate Virtual Environment

# On Linux/macOS
python3 -m venv .venv
source .venv/bin/activate

# On Windows
python -m venv .venv
.venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Train the Model

Ensure stud.csv is in the root directory, then run:

python train.py

This will generate the required model.pkl and preprocessor.pkl files inside the artifacts/ folder.

5. Start the Application

uvicorn main:app --reload

Open your browser and visit:

👉 [http://127.0.0.1:8000](http://127.0.0.1:8000)

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Docker Deployment

1. Build the Docker Image

docker build -t student-performance-app .

2. Run the Docker Container

docker run -p 8000:8000 student-performance-app

Access the application at http://localhost:8000.

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Deployment to AWS

To push and deploy this application on AWS:

1. Push to Amazon ECR:

aws ecr get-login-password --region <your-region> | docker login --username AWS --password-stdin <your-account-id>.dkr.ecr.<your-region>.amazonaws.com
docker tag student-performance-app:latest <your-account-id>.dkr.ecr.<your-region>.amazonaws.com/student-performance-app:latest
docker push <your-account-id>.dkr.ecr.<your-region>.amazonaws.com/student-performance-app:latest

2. Deploy Container: Launch via AWS App Runner or AWS ECS (Fargate) pointing directly to the image hosted in Amazon ECR.