Machine Learning Project Template 🤖

A professional Machine Learning project template designed for GitHub repositories connected to Kaggle. Use this as a starting point to keep your ML projects organized, reproducible, and production-ready.

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📂 Project Structure

.
├── data
│   ├── raw/              # Original datasets
│   └── processes/        # Cleaned and feature-engineered data
│
├── notebooks/            # Jupyter / Kaggle notebooks
│   └── ml.template.ipynb
│
├── src/
│   ├── __init__.py       # Register as a package
│   └── model_export.py   # Example code of model export
│
├── models/               # generated models
│
├── tests/                # Unit testing out the model
│
├── LICENSE.md            # License file
├── README.md             # Project documentation
├── requirements.txt      # Python dependencies
└── .gitignore            # Ignore python related dependencies and environments

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⚙️ Setup

You have two ways to use this template:

🔹 Option 1 — Work directly on Kaggle

1. Go to the Machine Learning Project Template repository on GitHub.

2. Click the “Use this template” button and create a new repository.

3. On Kaggle, create a new Notebook and choose “Link to GitHub” → select your repository.

   • This keeps your exploratory work synced with GitHub.

4. Start coding directly in Kaggle without installing anything locally.

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🔹 Option 2 — Work locally or on your own cloud machine

1. Go to the Machine Learning Project Template repository on GitHub.

2. Click the “Use this template” button and create a new repository.

3. Clone your new repository:

   git clone https://github.com/<your-username>/<your-repo-name>.git
   cd <your-repo-name>

4. Create a virtual environment:

   python -m venv .venv

5. Activate the virtual environment:

   • On Linux/Mac:

     source .venv/bin/activate

   • On Windows:

     venv\Scripts\activate

6. Install dependencies:

   pip install -r requirements.txt

Now you're good to go! 🚀

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📜 License

This project is licensed under the MIT License.