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Deciphering Customer Satisfaction: A Machine Learning-Oriented Method Using Agglomerative Clustering for Predictive Modeling and Feature Selection Cover

Deciphering Customer Satisfaction: A Machine Learning-Oriented Method Using Agglomerative Clustering for Predictive Modeling and Feature Selection

Management Systems in Production Engineering
Volume 33 (2025): Issue 1 (March 2025)
By: Nisrine Rezki,  Mohamed Mansouri and  Rachid Oucheikh  
Open Access
|Feb 2025

Authors

Nisrine Rezki

n.rezki@uhp.ac.ma

National School of Applied Sciences Berrechid, Hassan First University

Mohamed Mansouri

mohamed1969@yahoo.com

National School of Applied Sciences Berrechid, Hassan First University

Rachid Oucheikh

rachid.oucheikh@ju.se

Lund University
DOI: https://doi.org/10.2478/mspe-2025-0007 | Journal eISSN: 2450-5781 | Journal ISSN: 2299-0461
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Language: English
Page range: 60 - 70
Submitted on: May 1, 2024
Accepted on: Jan 1, 2025
Published on: Feb 17, 2025
Published by: STE Group sp. z.o.o.
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
SC management,
customer satisfaction,
machine learning,
SC disruption,
Agglomerative Clustering,
Random Forest Classifier
Related subjects:
Business and economics,
Business management,
Management accounting, financial controlling, cost calculation, investment,
Engineering,
Mechanical engineering,
Production technology,
Tools and methods,
Process engineering and industrial engineering

© 2025 Nisrine Rezki, Mohamed Mansouri, Rachid Oucheikh, published by STE Group sp. z.o.o.
This work is licensed under the Creative Commons Attribution 4.0 License.

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