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A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection

Customer churn is one of the most critical issues faced by the telecommunication industry (TCI). Researchers and analysts leverage customer relationship management (CRM) data through the use of various machine learning models and data transformation methods to identify the customers who are likely t...

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Detalles Bibliográficos
Autores principales: Sana, Joydeb Kumar, Abedin, Mohammad Zoynul, Rahman, M. Sohel, Rahman, M. Saifur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714823/
https://www.ncbi.nlm.nih.gov/pubmed/36454903
http://dx.doi.org/10.1371/journal.pone.0278095
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author Sana, Joydeb Kumar
Abedin, Mohammad Zoynul
Rahman, M. Sohel
Rahman, M. Saifur
author_facet Sana, Joydeb Kumar
Abedin, Mohammad Zoynul
Rahman, M. Sohel
Rahman, M. Saifur
author_sort Sana, Joydeb Kumar
collection PubMed
description Customer churn is one of the most critical issues faced by the telecommunication industry (TCI). Researchers and analysts leverage customer relationship management (CRM) data through the use of various machine learning models and data transformation methods to identify the customers who are likely to churn. While several studies have been conducted in the customer churn prediction (CCP) context in TCI, a review of performance of the various models stemming from these studies show a clear room for improvement. Therefore, to improve the accuracy of customer churn prediction in the telecommunication industry, we have investigated several machine learning models, as well as, data transformation methods. To optimize the prediction models, feature selection has been performed using univariate technique and the best hyperparameters have been selected using the grid search method. Subsequently, experiments have been conducted on several publicly available TCI datasets to assess the performance of our models in terms of the widely used evaluation metrics, such as AUC, precision, recall, and F-measure. Through a rigorous experimental study, we have demonstrated the benefit of applying data transformation methods as well as feature selection while training an optimized CCP model. Our proposed technique improved the prediction performance by up to 26.2% and 17% in terms of AUC and F-measure, respectively.
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spelling pubmed-97148232022-12-02 A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection Sana, Joydeb Kumar Abedin, Mohammad Zoynul Rahman, M. Sohel Rahman, M. Saifur PLoS One Research Article Customer churn is one of the most critical issues faced by the telecommunication industry (TCI). Researchers and analysts leverage customer relationship management (CRM) data through the use of various machine learning models and data transformation methods to identify the customers who are likely to churn. While several studies have been conducted in the customer churn prediction (CCP) context in TCI, a review of performance of the various models stemming from these studies show a clear room for improvement. Therefore, to improve the accuracy of customer churn prediction in the telecommunication industry, we have investigated several machine learning models, as well as, data transformation methods. To optimize the prediction models, feature selection has been performed using univariate technique and the best hyperparameters have been selected using the grid search method. Subsequently, experiments have been conducted on several publicly available TCI datasets to assess the performance of our models in terms of the widely used evaluation metrics, such as AUC, precision, recall, and F-measure. Through a rigorous experimental study, we have demonstrated the benefit of applying data transformation methods as well as feature selection while training an optimized CCP model. Our proposed technique improved the prediction performance by up to 26.2% and 17% in terms of AUC and F-measure, respectively. Public Library of Science 2022-12-01 /pmc/articles/PMC9714823/ /pubmed/36454903 http://dx.doi.org/10.1371/journal.pone.0278095 Text en © 2022 Sana 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, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Sana, Joydeb Kumar
Abedin, Mohammad Zoynul
Rahman, M. Sohel
Rahman, M. Saifur
A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection
title A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection
title_full A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection
title_fullStr A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection
title_full_unstemmed A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection
title_short A novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection
title_sort novel customer churn prediction model for the telecommunication industry using data transformation methods and feature selection
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9714823/
https://www.ncbi.nlm.nih.gov/pubmed/36454903
http://dx.doi.org/10.1371/journal.pone.0278095
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