Key Satisfaction Drivers Analysis based on Shapley values and Kano model
Project description
ShapKa: an open source python library to identify customer satisfaction drivers based on Shapley values and Kano model
Installation
Use the following command to install the package:
pip install ShapKa
Usage
Use the following command for a key dissatisfaction drivers analysis (kda) :
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 :
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
History
1.1.0 (2020-06-03)
Add weighting data functionnality.
1.0.0 (2019-10-25)
First release on PyPI.
Project details
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