Cookiecutter Kaggle

Attention: Work in Progress A logical, reasonably standardized, but flexible project structure for doing and sharing data science kaggle projects.

Based on the template by driven-data.

News:

The project will be presented and used at the Berlin Kaggle Meetup Group

Requirements

Python 2.7 or 3.5
cookiecutter Python package >= 1.4.0: pip install cookiecutter
Optional: Docker

Usage:

To initialize a new project after your system fulfills the requirements run:

cookiecutter https://github.com/uberwach/cookiecutter-kaggle

You can build the Docker image (based on the Kaggle Python3 Docker image) via:

docker build -t yourproject/tagname .

and then run an interactive shell via

docker run -i -v $PWD:/tmp/working \
  -w=/tmp/working -t yourproject/tagname \
  /bin/bash

on Windows you would use %cd% (current directory) instead of $PWD (print working directory).

You are asked to input data such as the project name and other uses of, say, the license. A project with the following file structure is being generated:

├── LICENSE
├── Makefile           <- Makefile with commands that perform parts of the processing pipeline
├── README.md          <- The top-level README
├── 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`.
│
├── 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`
├── Dockerfile         <- Dockerfile, alternative approach to manage environment
│                         more interesting if using non-Unix
├── submissions        <- Directory to keep submissions
│
├── 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 for submissions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│