Open Access

ARTICLE

Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

Olfat M. Mirza¹, G. Jose Moses², R. Rajender³, E. Laxmi Lydia⁴, Seifedine Kadry⁵, Cheadchai Me-Ead⁶, Orawit Thinnukool^7,*

1 Department of Computer Science, College of Computers and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia
2 Department of Computer Science Engineering, Malla Reddy Engineering College, 500100, India
3 Department of Computer Science Engineering, Lendi Institute of Engineering & Technology, Denkada, 535005, India
4 Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam, 530049, India
5 Department of Applied Data Science, Noroff University College, Norway
6 Program in Statistics and Information Management, Faculty of Science, Maejo University, Chiang Mai, 50290, Thailand
7 Department of Modern Management and Information Technology, College of Arts Media and Technology, Chiang Mai University, Chiang Mai, 50200, Thailand

* Corresponding Author: Orawit Thinnukool. Email:

Computers, Materials & Continua 2022, 73(2), 3757-3769. https://doi.org/10.32604/cmc.2022.030428

Received 25 March 2022; Accepted 07 May 2022; Issue published 16 June 2022

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 autoencoder based prediction (O-DCCAEP) model for competitive customer dependent application sector. In addition, the O-DCCAEP method purposes for determining the churning nature of the customers. The O-DCCAEP technique encompasses pre-processing, classification, and hyperparameter optimization. Additionally, the DCCAE model is employed to classify the churners or non-churner. Furthermore, the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm (DHOA). The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

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Keywords

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