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

├── 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.
│       └── README.md  <- Link or instructions on acquiring the data
│   ├── interim        <- Intermediate data that has been transformed, use standard naming convention.
│   ├── processed      <- The final, canonical data sets for modeling, snc refers to relevant notebooks.
│   └── raw            <- The original, immutable data dump.
│       └── README.md  <- Link or instructions on acquiring the data
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── logs               <- Stores details of experiments, learning curves from custom tools
│   └── ID-#-nb        <- Directory referencing the notebook or commit used to create logs.
|
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is the creator's initials,
│                         a number (for ordering), and a CamelCase description, e.g.
│                         `ak-1.0-DataPreprocessing.ipynb`.
│
├── references         <- Relevant papers, 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`
│
├── src                <- Source code for use in this project, the ultimate deliverable
│   ├── __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.testrun.org

pip install -r requirements.txt

py.test tests