uv tool install cookiecutter # Install cruft in a isolated environment
Or Install with pip
pip install --user cookiecutter # Install cookiecutter on your path for easy access
Note: Cookiecutter uses gh: as short-hand for https://github.com/
🔗 Linking an Existing Project
If the project was originally installed via [Cookiecutter], you must first use [Cruft] to link the project with the original template:
Then/else:
🗃️ Project structure
Folder structure for data science projects why?
[Data structure]
```bash . ├── .code_quality │ ├── mypy.ini # mypy configuration │ └── ruff.toml # ruff configuration ├── .github # github configuration │ ├── actions │ │ └── python-poetry-env │ │ └── action.yml # github action to setup python environment │ ├── dependabot.md # github action to update dependencies │ ├── pull_request_template.md # template for pull requests │ └── workflows # github actions workflows │ ├── ci.yml # run continuous integration (tests, pre-commit, etc.) │ ├── dependency_review.yml # review dependencies │ ├── docs.yml # build documentation (mkdocs) │ └── pre-commit_autoupdate.yml # update pre-commit hooks ├── .vscode # vscode configuration | ├── extensions.json # list of recommended extensions | ├── launch.json # vscode launch configuration | └── settings.json # vscode settings ├── conf # folder configuration files │ └── config.yaml # main configuration file ├── data │ ├── 01_raw # raw immutable data │ ├── 02_intermediate # typed data │ ├── 03_primary # domain model data │ ├── 04_feature # model features │ ├── 05_model_input # often called 'master tables' │ ├── 06_models # serialized models │ ├── 07_model_output # data generated by model runs │ ├── 08_reporting # reports, results, etc │ └── README.md # description of the data structure ├── docs # documentation for your project │ ├── index.md # documentation homepage ├── models # store final models ├── notebooks │ ├── 1-data # data extraction and cleaning │ ├── 2-exploration # exploratory data analysis (EDA) │ ├── 3-analysis # Statistical analysis, hypothesis testing. │ ├── 4-feat_eng # feature engineering (creation, selection, and transformation.) │ ├── 5-models # model training, experimentation, and hyperparameter tuning. │ ├── 6-evaluation # evaluation metrics, performance assessment │ ├── 7-deploy # model packaging, deployment strategies. │ ├── 8-reports # story telling, summaries and analysis conclusions. │ ├── notebook_template.ipynb # template for notebooks │ └── README.md # information about the notebooks