Metadata-Version: 2.1
Name: ShapKa
Version: 1.1.0
Summary: Key Satisfaction Drivers Analysis based on Shapley values and Kano model
Home-page: https://github.com/auyanik/ShapKa
Author: Ayhan UYANIK
Author-email: uyanik.ayhan@gmail.com
License: MIT license
Keywords: ShapKa
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: Unix
Classifier: Development Status :: 5 - Production/Stable
Requires-Python: >=3.5.*
Description-Content-Type: text/x-rst

======================================================================================================================
ShapKa: an open source python library to identify customer satisfaction drivers based on Shapley values and Kano model
======================================================================================================================


.. image:: https://img.shields.io/pypi/v/shapleykano.svg
        :target: https://pypi.python.org/pypi/shapleykano

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.. image:: https://travis-ci.org/uyanik/shapleykano.svg?branch=master
        :target: https://travis-ci.org/uyanik/shapleykano

.. image:: https://readthedocs.org/projects/shapleykano/badge/?version=latest
        :target: https://shapleykano.readthedocs.io/en/latest/?badge=latest
        :alt: Documentation Status



Installation
------------
Use the following command to install the package:

.. code-block:: python

   pip install ShapKa


Usage
-----
Use the following command for a key dissatisfaction drivers analysis (kda) :

.. code-block:: python 

    import pandas as pd
    from ShapKa.kanomodel import KanoModel

    # Load data
    df = pd.read_csv('data/example_03.csv')

    # Define X and Y variables names
    y_varname = 'Overall Satisfaction'
    weight_varname = 'Weight'
    X_varnames = df.columns.values.tolist()
    X_varnames.remove(y_varname)
    X_varnames.remove(weight_varname)

    # Run analysis to identify key dissatisfiers
    model = KanoModel(df, 
                      y_varname, X_varnames, 
                      analysis = 'kda',
                      y_dissat_upperbound = 6, y_sat_lowerbound = 9,
                      X_dissat_upperbound = 6, X_sat_lowerbound = 9,
                      weight_varname = weight_varname)

    kda = model.key_drivers() ;kda


Here is the ouput :

.. image:: img/output_ShapKa.png
        :width: 600

Replace 'kda' by 'kea' in the analysis parameter if you want to identify key enhancers (kea) instead of key dissatisfiers


Documentation
-------------
* Documentation: https://shapleykano.readthedocs.io.


Credits
-------

* The ShapKa package is based on the methodology developped by `W. Michael Conklin, Ken Powaga and Stan Lipovetsky`_

* Some parts of the code are based on functions implemented in the `Open Source Sage Mathematical Software`_


References
----------
* Conklin, Michael & Powaga, Ken & Lipovetsky, Stan. (2004). Customer satisfaction analysis: Identification of key drivers. European Journal of Operational Research. 154. 819-827. 10.1016/S0377-2217(02)00877-9. 

* Sage - Open Source Mathematical Software : `https://github.com/sagemath/sage`_

.. _`W. Michael Conklin, Ken Powaga and Stan Lipovetsky`: https://www.researchgate.net/publication/222399844_Customer_satisfaction_analysis_Identification_of_key_drivers

.. _`https://github.com/sagemath/sage`: https://github.com/sagemath/sage

.. _`Open Source Sage Mathematical Software`: https://github.com/sagemath/sage


=======
History
=======

1.1.0 (2020-06-03)
------------------

* Add weighting data functionnality.

1.0.0 (2019-10-25)
------------------

* First release on PyPI.


