Credit Card Service Churn Prediction by Machine Learning Models
Main Article Content
Abstract
This paper presents a study on the application of basic machine learning models for churn customer classification. Churn prediction is an essential task in customer retention for businesses, and accurate identification of customers who are likely to churn can significantly impact the organization's revenue and customer satisfaction. In this study, we explore the performance of various machine learning models, including K-Nearest Neighbor, Random Forest, Adaboost and a deep learning model which is CNN-1D. We use the BankChurners dataset, then we predict the probability that customers abandoning bank services such as credit card services. We evaluate the models basing on various performance metrics such as accuracy, precision, recall, and F1-score. The result demonstrates the potential of basic machine learning models for churn customer classification and provides insights into the key factors contributing to customer churn.
Keywords
Prediction, business analysis, machine learning, E-commerce.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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[14] Churn for Bank Customers. [Online] Available at: https://www.kaggle.com/datasets/mathchi/churn-forbank-customers (Accessed 25/12/2023).
[2] J. Burez and D. Van den Poel, Handling class imbalance in customer churn prediction, Expert Syst. Appl. 36 (3), 2009, 4626-4636, https://doi.org/10.1016/jeswa.2008.05.027
[3] Panimalar, S. A. and Krishnakumar, A., A review of churn prediction models using different machine learning and deep learning approaches in cloud environment, Journal of Current Science and Technology, 13(1), 2023, 136-161. [4] N. Gordini, A genetic algorithm approach for SMEs bankruptcy prediction: Empirical evidence from Italy, Expert Syst. Appl. 41 (14), 2014, 6433-6445, https://doi.org/10.1016/j.eswa.2014.04.026
[5] A. Lemmens and C. Croux, Bagging and boosting classification trees to predict churn, J. Market. Res. 43 (2), 2006, 276-286,
[6] Michael C. Mozer, Richard Wolniewicz, David B. Grimes, Eric Johnson, and Howard Kaushansky, Churn reduction in the wireless industry, In Proceedings of the 12th International Conference on Neural Information Processing Systems (NIPS'99). MIT Press, Cambridge, MA, USA, 1999, 935-941.
[7] Risselada Hans, Verhoef Peter C and Bijmolt Tammo H.A, Staying power of churn prediction models, Journal of Interactive Marketing, Elsevier, vol. 24(3), 2010, 198-208.
[8] Martínez-López Francisco and Casillas Jorge, Artificial intelligence-based systems applied in industrial marketing: An historical overview, current and future insights, Industrial Marketing Management, 42, 2013, 489-495.
[9] Tamaddoni Ali, Stakhovych Stanislav, and Ewing Michael, Comparing churn prediction techniques and assessing their performance: A contingent perspective, Journal of Service Research. 19, 2015, https://doi.org/10.1177/1094670515616376.
[10] Coussement Kristof and De Bock Koen, Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning, Journal of Business Research, 66, 2013, 1629-1636, https://doi.org/DOI: 10.1016/j.jbusres.2012.12.008.
[11] Wai-Ho Au, K. C. C. Chan, and Xin Yao, A novel evolutionary data mining algorithm with applications to churn prediction, in IEEE Transactions on Evolutionary Computation, vol. 7, no. 6, 2003, 532-545, https://doi.org/10.1109/TEVC.2003.819264.
[12] P. C. Pendharkar, Genetic algorithm based neural network approaches for predicting churn in cellular wireless network services, Expert Syst. Appl. 36 (3), 2009, 6714-6720, https://doi.org/10.1016/j.eswa.2008.08.050.
[13] A. Sharma and P. K. Panigrahi, A neural network based approach for predicting customer churn in cellular network services, Int. J. Comput. Appl. 27 (11), 2011, 26-31, https://doi.org/10.5120/3344-4605.
[14] Churn for Bank Customers. [Online] Available at: https://www.kaggle.com/datasets/mathchi/churn-forbank-customers (Accessed 25/12/2023).