🚀 ML Python Pipeline: Logistic Regression (GLM)

ML API that predicts binary classes using scikit-learn and FastAPI.

• Model trained on breast cancer dataset
• REST API with FastAPI - real-time requests
• Docker support
• Environment variable configuration
• Citable research-ready API
• MIT Licensed

📂 Project Structure

.
├── app/                # FastAPI application
├── train/              # Training scripts
├── assets/images/      # Images, diagrams
├── requirements.txt    # Python dependencies
├── Dockerfile
├── .env.dist           # Sample environment variables
├── LICENSE.md
└── README.md

🔋 Run Locally

1. Clone the repository:

git clone https://github.com/your-username/Python_GML_MLPipeline.git
cd Python_GML_MLPipeline

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate

3. Install dependencies:

pip install -r requirements.txt

4. Copy .env.dist to .env and configure your environment variables:

PORT=8000
MODEL_PATH=train/model.pkl

5. Train the model:

cd train/
python train_model.py
cd ..

6. Start the server:

uvicorn app.main:app --reload --port $PORT

🐟 Docker

1. Build the Docker image:

docker build -t ml-api .

2. Run the container:

docker run --env-file .env -p 8000:8000 ml-api

Visit http://127.0.0.1:8000/docs for Swagger UI.

👋 Author

Made with ❤️ by Pierre-Henry Soria. A super passionate & enthusiastic Problem-Solver / Senior Software Engineer. Also a true cheese 🧀, ristretto ☕️, and dark chocolate lover! 😋

📄 License

Distributed under the MIT License 🎉 Happy hacking! 🤠