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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.

🤝 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.