Ecosystems:
Python
Use https://github.com/MaxHalford/Prince instead
skmca
A scikit-learn pipeline API compatible implementation of Multiple Correspondence Analysis (MCA).
Usage
.. code-block:: python
import pandas as pd
from skmca import MCA
df = pd.read_csv('http://www.statoek.wiso.uni-goettingen.de/'
'CARME-N/download/wg93.txt',
sep='\t', dtype='category')
mca = MCA()
mca.fit(df)
Crucially, the input to ``MCA.fit`` must be a ``pandas.DataFrame``
where all the columns have a ``category`` dtype. This is necessary
to ensure that the dummy encoding of the columns is consistent across
training and test datasets.
Background
MCA is like PCA_, but for categorical data.
You can use it to visualize high-dimensional datasets.
It can also be useful as a pre-processing step for clustering,
to avoid the curse of dimensionality.
skmca requires pandas and scikit-learn.
References
This library follows the setup in `Nenadic and Greenacre (2005)`_.
.. PCA: http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
.. Nenadic and Greenacre (2005): https://core.ac.uk/download/pdf/6591520.pdf