ml_project/docs/getting-started.md

Getting Started

Prerequisites

• Docker
• Python 3.7+

Installation

1. Clone the repository
2. Build the Docker image:

docker build -t test-score-predictor .

3. Run the container:

docker run -p 8000:8000 test-score-predictor

Environment Variables

The application uses the following environment variables:

• MODEL_PATH: Path to the model file (default: linear_regression_model.pkl)
• SCALER_PATH: Path to the scaler file (default: scaler.pkl)
• PORT: Port to run the application on (default: 8000)

These can be set in the .env file or passed as environment variables.

Development

To set up a development environment:

1. Create a virtual environment:

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

2. Install dependencies:

pip install -r requirements.txt

3. Run the application:

uvicorn main:app --reload

The application will be available at http://localhost:8000.

Testing

Run the tests using:

pytest test_api.py

Deployment

The application includes a GitHub Actions workflow for CI/CD in the .github/workflows/deploy.yml file.