https://github.com/esafak/mca
Multiple correspondence analysis
https://github.com/esafak/mca
statistics
Last synced: 11 months ago
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Multiple correspondence analysis
- Host: GitHub
- URL: https://github.com/esafak/mca
- Owner: esafak
- License: bsd-3-clause
- Created: 2014-06-05T05:12:04.000Z (about 12 years ago)
- Default Branch: master
- Last Pushed: 2022-12-26T22:36:46.000Z (over 3 years ago)
- Last Synced: 2024-11-09T13:20:17.001Z (over 1 year ago)
- Topics: statistics
- Language: Python
- Size: 123 KB
- Stars: 179
- Watchers: 16
- Forks: 73
- Open Issues: 3
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Metadata Files:
- Readme: README.rst
- Changelog: HISTORY.rst
- Contributing: CONTRIBUTING.rst
- License: LICENSE
Awesome Lists containing this project
README
===============================
mca
===============================
.. image:: https://badge.fury.io/py/mca.png
:target: https://pypi.org/project/mca/
.. image:: https://img.shields.io/github/actions/workflow/status/esafak/mca/test_mca.yaml
:target: https://github.com/esafak/mca/actions/workflows/test_mca.yaml
mca is a `Multiple Correspondence Analysis `_ (MCA) package for python, intended to be used with `pandas `_. MCA is a `feature extraction `_ method; essentially `PCA `_ for `categorical variables `_. You can use it, for example, to address `multicollinearity `_ or the `curse of dimensionality `_ with big categorical variables.
Installation
------------
.. code :: bash
pip install --user mca
Usage
-----
Please refer to the `usage notes `_ and `this illustrated ipython notebook `_.
References
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* Michael Greenacre, Jörg Blasius (2006). `Multiple Correspondence Analysis and Related Methods `_, CRC Press. ISBN 1584886285.
* François Husson, `Multiple Correspondence Analysis Youtube Playlist `_, Youtube