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An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry
Mobile communication has become a dominant medium of communication over the past two decades. New technologies and competitors are emerging rapidly and churn prediction has become a great concern for telecom companies. A customer churn prediction model can provide the accurate identification of pote...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044233/ https://www.ncbi.nlm.nih.gov/pubmed/35494841 http://dx.doi.org/10.7717/peerj-cs.854 |
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author | Fakhar Bilal, Syed Ali Almazroi, Abdulwahab Bashir, Saba Hassan Khan, Farhan Ali Almazroi, Abdulaleem |
author_facet | Fakhar Bilal, Syed Ali Almazroi, Abdulwahab Bashir, Saba Hassan Khan, Farhan Ali Almazroi, Abdulaleem |
author_sort | Fakhar Bilal, Syed |
collection | PubMed |
description | Mobile communication has become a dominant medium of communication over the past two decades. New technologies and competitors are emerging rapidly and churn prediction has become a great concern for telecom companies. A customer churn prediction model can provide the accurate identification of potential churners so that a retention solution may be provided to them. The proposed churn prediction model is a hybrid model that is based on a combination of clustering and classification algorithms using an ensemble. First, different clustering algorithms (i.e. K-means, K-medoids, X-means and random clustering) were evaluated individually on two churn prediction datasets. Then hybrid models were introduced by combining the clusters with seven different classification algorithms individually and then evaluations were performed using ensembles. The proposed research was evaluated on two different benchmark telecom data sets obtained from GitHub and Bigml platforms. The analysis of results indicated that the proposed model attained the highest prediction accuracy of 94.7% on the GitHub dataset and 92.43% on the Bigml dataset. State of the art comparison was also performed using the proposed model. The proposed model performed significantly better than state of the art churn prediction models. |
format | Online Article Text |
id | pubmed-9044233 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-90442332022-04-28 An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry Fakhar Bilal, Syed Ali Almazroi, Abdulwahab Bashir, Saba Hassan Khan, Farhan Ali Almazroi, Abdulaleem PeerJ Comput Sci Artificial Intelligence Mobile communication has become a dominant medium of communication over the past two decades. New technologies and competitors are emerging rapidly and churn prediction has become a great concern for telecom companies. A customer churn prediction model can provide the accurate identification of potential churners so that a retention solution may be provided to them. The proposed churn prediction model is a hybrid model that is based on a combination of clustering and classification algorithms using an ensemble. First, different clustering algorithms (i.e. K-means, K-medoids, X-means and random clustering) were evaluated individually on two churn prediction datasets. Then hybrid models were introduced by combining the clusters with seven different classification algorithms individually and then evaluations were performed using ensembles. The proposed research was evaluated on two different benchmark telecom data sets obtained from GitHub and Bigml platforms. The analysis of results indicated that the proposed model attained the highest prediction accuracy of 94.7% on the GitHub dataset and 92.43% on the Bigml dataset. State of the art comparison was also performed using the proposed model. The proposed model performed significantly better than state of the art churn prediction models. PeerJ Inc. 2022-02-22 /pmc/articles/PMC9044233/ /pubmed/35494841 http://dx.doi.org/10.7717/peerj-cs.854 Text en © 2022 Fakhar Bilal et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Fakhar Bilal, Syed Ali Almazroi, Abdulwahab Bashir, Saba Hassan Khan, Farhan Ali Almazroi, Abdulaleem An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry |
title | An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry |
title_full | An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry |
title_fullStr | An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry |
title_full_unstemmed | An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry |
title_short | An ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry |
title_sort | ensemble based approach using a combination of clustering and classification algorithms to enhance customer churn prediction in telecom industry |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9044233/ https://www.ncbi.nlm.nih.gov/pubmed/35494841 http://dx.doi.org/10.7717/peerj-cs.854 |
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