Awesome

Cookiecutter Data Science Template [crplab]

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

Cookiecutter Data Science is a real game changer for data science projects. Thanks to @drivendata and @vasinkd for the base of this template.

HOW TO USE:

First of all, install cookiecutter with:

$ brew install cookiecutter  # or pip install cookiecutter

After that you can use template with:

$ cookiecutter https://github.com/crplab/cdst

The resulting directory structure

The directory structure of your new project will look like this:

├── LICENSE
├── Makefile                    <- Makefile with commands like `make init` or `make clean`
├── README.md                   <- The top-level README for developers using this project.
├── data
│   ├── external                <- Data from third party sources.
│   ├── interim                 <- Intermediate data that has been transformed.
│   ├── processed               <- The final, canonical data sets for modeling.
│   ├── features                <- Features may be stored here
│   ├── inference               <- Inference stages may be stored here
│   └── raw                     <- The original, immutable data dump.
│
├── docs                        <- A default Sphinx project; see sphinx-doc.org for details
│
├── models                      <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks                   <- Jupyter notebooks. Naming convention is a number (for ordering),
│                               the creator's initials, and a short `-` delimited description, e.g.
│                               `1.0-jqp-initial-data-exploration`.
│
├── references                  <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports                     <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures                 <- Generated graphics and figures to be used in reporting
│
├── conda.yml                   <- conda environment definition
│   
├── .pre-commit-config.yaml     <- pre-commit configuration
│
├── requirements.txt            <- The requirements file for reproducing the analysis environment, e.g.
│                               generated with `pip freeze > requirements.txt`
│   
├── __init__.py
│
└── {{cookiecutter.repo_name}}
    ├── __init__.py             <- Makes {{cookiecutter.repo_name}} a Python module
    ├── settings.py             <- illustrates how to use .env file
    ├── data                    <- Scripts to download or generate data
    │   └── make_dataset.py
    ├── features                <- Scripts to turn raw data into features for modeling
    │   └── featurize.py
    └── models                  <- Scripts to train models and then use trained models to make
        │                       predictions
        └── train.py