Equancy Cookiecutter Data Science Project

Flexible data science project structure for doing data science use case.

Requirements to use the cookiecutter template:

---

• Python 2.7 or 3.x
• Cookiecutter Python package >= 1.4.0: This can be installed with pip by or conda depending on how you manage your Python packages:

$ pip install cookiecutter

or

$ conda config --add channels conda-forge
$ conda install cookiecutter

To start a new project, run:

---

cookiecutter http://github.com/equancy/cookiecutter-data-science-project

The resulting directory structure

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The directory structure of your new project looks like this:

├── LICENSE            <- Equancy all rights reserved.
├── Makefile           <- Makefile with commands like `make data` or `make train`.
├── README.md          <- The top-level README for developers using this project.
│
├── _can_be_deleted    <- Trash bin (!! git ignored)
│
├── confidential       <- Confidential documents, data, etc. (!! git ignored)
│
├── data               <- !! git ignored
│   ├── external       <- Data from third party sources.
│   ├── processed      <- The final, canonical data sets for modeling.
│   ├── raw            <- The original, immutable data dump.
│   └── working        <- Working, intermediate data that has been transformed.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│                         Also includes sklearn & pyspark pipelines.
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-rvm-initial-data-exploration`.
│
├── production
│   ├── config         <- YAML files with dependancies between tasks, data catalog and others.
│   ├── pipelines      <- sklearn & pyspark pipelines.
│   ├── tasks          <- Luigi tasks.
│   └── scripts        <- Functions used by Luigi tasks.
│
├── 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`
│
├── 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.testrun.org

Installing development requirements

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pip install -r requirements.txt

Running the tests

---

py.test tests

Inspiration

From DrivenData.