ml_project/docs/getting-started.md

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# Getting Started
## Prerequisites
- Docker
- Python 3.7+
## Installation
1. Clone the repository
2. Build the Docker image:
```bash
docker build -t test-score-predictor .
```
3. Run the container:
```bash
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:
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Run the application:
```bash
uvicorn main:app --reload
```
The application will be available at http://localhost:8000.
## Testing
Run the tests using:
```bash
pytest test_api.py
```
## Deployment
The application includes a GitHub Actions workflow for CI/CD in the `.github/workflows/deploy.yml` file.