Semi-Automated machine learning/data science workflow


Keywords
automation, data-science, data-visualization, machine-learning, python3, workflow
License
MIT
Install
pip install eflow==0.2.46.15

Documentation

eflow

I designed this project to help make my life easier for my data science projects. It uses a combination of generating code in cell blocks (Check my testing/templates for example notebooks) and well designed objects for analysis and modeling. Currently it is far from done; (studying for GRE and have a full time job in my defense: ) ).

This project was mainly designed for and in Jupyter-Lab. I don't know how well it works in Jupyter-Notebook.

Installation

$ pip install eflow

Other commands/steps to make the project work properly

Ensuring the widgets work properly

$ jupyterlab nbextension enable --py widgetsnbextension --sys-prefix
$ jupyter nbextension enable --py widgetsnbextension --sys-prefix
$ jupyter labextension install @jupyter-widgets/jupyterlab-manager
$ jupyter lab clean
$ jupyter lab build

Getting natural language datasets setup. Start by opening up a Python Repl.

$ python
>>> nltk.download('wordnet')
>>> nltk.download('words')
>>> nltk.download('punkt')

Project Requirements

  • Python >= 3.7
  • Latest version's of the following packages:
    • jupyterlab
    • numpy
    • pandas
    • missingno
    • matplotlib
    • seaborn
    • sklearn
    • kneed
    • Pillow
    • tqdm
    • python-dateutil
    • scikit-plot
    • scipy
    • ipywidgets
    • ipython_blocking
    • nltk