.. image:: https://badge.fury.io/py/mca.png :target: http://badge.fury.io/py/mca
.. image:: https://travis-ci.org/esafak/mca.png?branch=master :target: https://travis-ci.org/esafak/mca
mca is a Multiple Correspondence Analysis <http://en.wikipedia.org/wiki/Multiple_correspondence_analysis>
(MCA) package for python, intended to be used with pandas <http://pandas.pydata.org/>
. MCA is a feature extraction <http://en.wikipedia.org/wiki/Feature_extraction>
_ method; essentially PCA <http://en.wikipedia.org/wiki/Principal_component_analysis>
_ for categorical variables <http://en.wikipedia.org/wiki/Categorical_variable>
. You can use it, for example, to address multicollinearity <http://en.wikipedia.org/wiki/Multicollinearity>
or the curse of dimensionality <http://en.wikipedia.org/wiki/Curse_of_dimensionality>
_ with big categorical variables.
.. code :: bash
pip install --user mca
Please refer to the usage notes <https://github.com/esafak/mca/blob/master/docs/usage.rst>
and this illustrated ipython notebook <http://nbviewer.ipython.org/github/esafak/mca/blob/master/docs/mca-BurgundiesExample.ipynb>
.
Michael Greenacre, Jörg Blasius (2006). Multiple Correspondence Analysis and Related Methods <http://www.crcpress.com/product/isbn/9781584886280>
_, CRC Press. ISBN 1584886285.