https://github.com/jlehrer1/cookiecutter-minimal-datascience

A cookiecutter project template for simple, competitive data science projects
https://github.com/jlehrer1/cookiecutter-minimal-datascience

cookiecutter data-science minimal python

Last synced: about 1 month ago
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A cookiecutter project template for simple, competitive data science projects

• Host: GitHub
• URL: https://github.com/jlehrer1/cookiecutter-minimal-datascience
• Owner: jlehrer1
• License: mit
• Created: 2020-07-11T04:25:32.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2020-08-01T05:54:34.000Z (almost 6 years ago)
• Last Synced: 2025-11-10T09:12:33.106Z (8 months ago)
• Topics: cookiecutter, data-science, minimal, python
• Homepage:
• Size: 39.1 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Cookiecutter: Minimal Datascience

Similar to the default provided by DrivenData, but simplified for casual project usage. 

To use, run

```

cookiecutter https://github.com/jlehrer1/cookiecutter-minimal-datascience

```

I highly recommend aliasing the above command.

The structure of your project will be as follows

```

├── LICENSE

├── 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.

│

├── 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`.

│

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

│                         created with "conda env export > environment.yml"

│

├── run.py             <- Script for running the full analysis 

│                         

├── 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

```