Cookiecutter for Data Science Projects

A powerful and user-friendly Cookiecutter template for Data Science projects.

Requirements

• Conda
• Cookiecutter Python package: This can be installed with pip by or conda depending on how you manage your Python packages:

pip install cookiecutter

or

conda install -c conda-forge cookiecutter

Create a new project

In a folder where you want your generate your project:

cookiecutter https://github.com/royquillca/cookiecutter-ds

Demo

Resulting directory structure

├── LICENSE
├── README.md          <- The top-level README for developers using this project.
├── Installation.md    <- Detailed instructions to set up 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.
│
├── 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
│   └── 0.0-example-data-exploratory.ipynb  <- Example notebook ready to be used as script
│   └── 1.0-{{cookiecutter.project_author_name}}-data-cleaning.ipynb                        
│   └── 2.0-{{cookiecutter.project_author_name}}-data-exploration.ipynb                        
│   └── 2.0-{{cookiecutter.project_author_name}}-data-preprocessing.ipynb                        
│   └── 2.0-{{cookiecutter.project_author_name}}-feature-selection.ipynb                        
│   └── 2.0-{{cookiecutter.project_author_name}}-model-training.ipynb                        
│   └── 2.0-{{cookiecutter.project_author_name}}-model-evaluation-and-optimization.ipynb                        
│   └── 2.0-{{cookiecutter.project_author_name}}-monitoring-and-maintenance.ipynb   
│            
├── 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.
│
├── environment.yml    <- The requirements file for reproducing the analysis environment.
│
├── .here              <- File that will stop the search if none of the other criteria
│                         apply when searching head of project.
│
├── setup.py           <- Makes project pip installable (pip install -e .)
│                         so {{ cookiecutter.project_module_name }} can be imported.
│
└── {{ cookiecutter.project_module_name }}               <- Source code for use in this project. Default src/
    ├── __init__.py    <- Makes {{ cookiecutter.project_module_name }} 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
    │
    ├── utils          <- Scripts to help with common tasks.
        └── paths.py   <- Helper functions to relative file referencing across project.
    │
    └── visualization  <- Scripts to create exploratory and results oriented visualizations.
        └── visualize.py

Contributing guide

All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.

Credits

This project is heavily influenced by drivendata's Cookiecutter Data Science, andfanilo's Cookiecutter for Kaggle Conda projects, and julia's package DrWatson.

Other links that helped shape this cookiecutter :

• Write less terrible code with Jupyter Notebook
• Cookiecutter DataScience Opinions