Machine Learning Playground

An end‑to‑end ML workflow on the Titanic dataset, demonstrating data loading, EDA, model training, evaluation, serving, containerization, and CI.

🚀 Features

• Exploratory Data Analysis
  Interactive Jupyter notebook with data profiling and visualizations.

• Modular Codebase
  Python modules under src/ for data, features, models, training, evaluation, and API.

• Hyperparameter Tuning
  Grid search over model parameters (configurable via config/train_config.yaml).

• Model Serving
  FastAPI endpoint at /predict for live inference.

• Containerization
  Dockerfile to build and run the app in a container.

• CI/CD
  GitHub Actions workflow for linting (flake8) and testing (pytest) on every push.

📦 Installation

1. Clone the repo

   git clone https://github.com/<your-username>/ml-playground.git
   cd ml-playground

2. Create & activate a virtual environment

   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

📊 Exploratory Data Analysis

Open the Jupyter notebook:

jupyter notebook notebooks/exploratory_data_analysis.ipynb

🤖 Training & Evaluation

• Training:

  python src/train.py --config config/train_config.yaml

• Evaluation:

  python src/evaluate.py --model-path models/best_model.pkl

🚀 Serving the Model

Start FastAPI server:

uvicorn src.api:app --reload

• Endpoint: POST /predict with JSON payload of features.

🐳 Docker

Build and run:

docker build -t ml-playground .
docker run -p 8000:8000 ml-playground

✅ CI/CD

GitHub Actions workflow (.github/workflows/ci.yaml) auto‑runs flake8 and pytest on every push.

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🤝 Contributing

Feel free to open issues or submit PRs to improve the pipeline, add new models, or enhance deployment.

📄 License

This project is MIT‑licensed. See LICENSE for details.