Customer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural Network Algorithms
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Advanced Engineering and Science - Indonesian Journal of Electrical Engineering and Informatics, 10(2), 431 - 440
Abstract
Customer 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