Olfat M. Mirza1, G. Jose Moses2, R. Rajender3, E. Laxmi Lydia4, Seifedine Kadry5, Cheadchai Me-Ead6, Orawit Thinnukool7,*
CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3757-3769, 2022, DOI:10.32604/cmc.2022.030428
Abstract Presently, customer retention is essential for reducing customer churn in telecommunication industry. Customer churn prediction (CCP) is important to predict the possibility of customer retention in the quality of services. Since risks of customer churn also get essential, the rise of machine learning (ML) models can be employed to investigate the characteristics of customer behavior. Besides, deep learning (DL) models help in prediction of the customer behavior based characteristic data. Since the DL models necessitate hyperparameter modelling and effort, the process is difficult for research communities and business people. In this view, this study designs an optimal deep canonically correlated… More >
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