Author:
Kharat Sandhya S.,Rane Charushila V.
Publisher
Springer Nature Switzerland
Reference6 articles.
1. Y. Deng, D. Li, L. Yang, J. Tang, J. Zhao, Analysis and prediction of bank user churn basedon ensemble learning algorithm, in 2021 IEEE International Conference on Power Electronics,Computer Applications (ICPECA), (IEEE, 2021, January), pp. 288–291
2. Y. Beeharry, R. Tsokizep Fokone, Hybrid approach using machine learning algorithms forcustomers’ churn prediction in the telecommunications industry. Concurrency Comput. Pract.Exper. 34(4), e6627 (2022)
3. M. Zhao, Q. Zeng, M. Chang, Q. Tong, J. Su, A prediction model of customer churn considering customer value: An empirical research of telecom industry in China. Discret. Dyn. Nat. Soc. 2021, 1–12 (2021)
4. C.F. Tsai, M.Y. Chen, Variable selection by association rules forcustomer churn prediction of multimedia on demand.Exp. Syst. Appl. 37(3), 2006–2015 (2010)
5. D. Anil Kumar, V. Ravi, Predicting credit card customer churn in banks using data mining. Int. J. Data Anal. Tech. Strateg. 1(1), 4–28 (2008)