registries_analysis/README.md

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data-registries
==============================
A short description of the project.
Project Organization
------------
├── 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
--------
<p><small>Project based on the <a target="_blank" href="https://drivendata.github.io/cookiecutter-data-science/">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>