This is a template cookiecutter project for bootstrapping your work on ML data science projects. It contains :

• a directory structure for sorting your notebooks, data, models, figures, tasks and source code to reuse in notebooks
• a conda environment file with the basic python libraries and some extras :
  • numpy / pandas / scikit-learn / seaborn / statsmodels / plotly / jupyterlab classic Data Science stack
  • streamlit for building and run top to bottom data apps
  • pyspark and h2o for distributed processing
  • pandas-profiling for generating HTML reports on pandas dataframes
  • missingno for missing data analysis
  • invoke as a replacement to Makefile for managing project tasks
  • nbdime for diffing and merging notebooks
  • kaggle-api a CLI for interacting with Kaggle API
  • keras and lightgbm for prediction
  • path.py for browsing files in Python

• Anaconda >=5.x
• Cookiecutter >= 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

In a folder where you want your project generated : cookiecutter https://github.com/flight505/la-cookiecutter

You can also clone the project in <path/to/template>, and from the folder where you want to generate your project, launch cookiecutter <path/to/template>

It will ask for the following values :

full_name
email
project_name
project_slug
project_short_description
version

(project_slug is the name you will use to install your package elsewere using pip install your_package_name)

Complete the values for your project and voilà ! Then follow the README inside your new project for further installation.

All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.

This is a Fork from andfanilo, which was modified for Streamlit applications

This project is heavily influenced by drivendata's Data Science cookiecutter.

Other links that helped shape this cookiecutter :

• Write less terrible code with Jupyter Notebook
• Cookiecutter DataScience Opinions