Customer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural Network Algorithms

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dc.contributor.authorAbdulsalam, S.O.
dc.contributor.authorArowolo, M.O.
dc.contributor.authorSaheed, Y.K.
dc.contributor.authorAfolayan, J.O.
dc.date.accessioned2025-10-29T11:35:53Z
dc.date.available2025-10-29T11:35:53Z
dc.date.issued2022
dc.description.abstractCustomer churn is a serious problem, which is a critical issue encountered by large businesses and organizations. Due to the direct impact on the company's revenues, particularly in sectors such as the telecommunications as well as the banking, companies are working to promote ways to identify the churn of prospective consumers. Hence it is vital to investigate issues that influence customer churn to yield appropriate measures to diminish churn. The major objective of this work is to advance a model of churn prediction that helps telecom operatives to envisage clients that are most probable to be subjected to churn. The experimental approach for this study uses the machine learning procedures on the telecom churn dataset, using an improved Relief-F feature selection algorithm to pick related features from the huge dataset. To quantify the model's performance, the result of classification uses CART and ANN, the accuracy shows that ANN has a high predictive capacity of 93.88% compared to the 91.60% CART classifier. Keywords: Telecoms, Relief-F, ANN, CART, Churn
dc.identifier.urihttps://kwasuspace.kwasu.edu.ng/handle/123456789/6238
dc.language.isoen
dc.publisherInstitute of Advanced Engineering and Science - Indonesian Journal of Electrical Engineering and Informatics, 10(2), 431 - 440
dc.titleCustomer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural Network Algorithms
dc.typeArticle
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