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https://github.com/drivendata/cookiecutter-data-science

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
https://github.com/drivendata/cookiecutter-data-science

ai cookiecutter cookiecutter-data-science cookiecutter-template data-science machine-learning

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

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# Cookiecutter Data Science



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



#### [Project homepage](http://drivendata.github.io/cookiecutter-data-science/)



### Requirements to use the cookiecutter template:

-----------

 - Python 2.7 or 3.5+

 - [Cookiecutter Python package](http://cookiecutter.readthedocs.org/en/latest/installation.html) >= 1.4.0: This can be installed with pip by or conda depending on how you manage your Python packages:



``` bash

$ pip install cookiecutter

```



or



``` bash

$ conda config --add channels conda-forge

$ conda install cookiecutter

```



### To start a new project, run:

------------



    cookiecutter -c v1 https://github.com/drivendata/cookiecutter-data-science



[![asciicast](https://asciinema.org/a/244658.svg)](https://asciinema.org/a/244658)



### New version of Cookiecutter Data Science

------------

Cookiecutter data science is moving to v2 soon, which will entail using

the command `ccds ...` rather than `cookiecutter ...`. The cookiecutter command

will continue to work, and this version of the template will still be available.

To use the legacy template, you will need to explicitly use `-c v1` to select it.

Please update any scripts/automation you have to append the `-c v1` option (as above),

which is available now.



### The resulting directory structure

------------



The directory structure of your new project looks like this: 



```

├── LICENSE

├── Makefile           <- Makefile with commands like `make data` or `make train`

├── 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.

│   └── 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

│

├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.

│                         generated with `pip freeze > requirements.txt`

│

├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported

├── src                <- Source code for use in this project.

│   ├── __init__.py    <- Makes src a Python module

│   │

│   ├── data           <- Scripts to download or generate data

│   │   └── make_dataset.py

│   │

│   ├── features       <- Scripts to turn raw data into features for modeling

│   │   └── build_features.py

│   │

│   ├── models         <- Scripts to train models and then use trained models to make

│   │   │                 predictions

│   │   ├── predict_model.py

│   │   └── train_model.py

│   │

│   └── visualization  <- Scripts to create exploratory and results oriented visualizations

│       └── visualize.py

│

└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

```



## Contributing



We welcome contributions! [See the docs for guidelines](https://drivendata.github.io/cookiecutter-data-science/#contributing).



### Installing development requirements

------------



    pip install -r requirements.txt



### Running the tests

------------



    py.test tests