Publisher
Springer Nature Singapore
Reference20 articles.
1. Raeisi S, Sajedi H (2020) E-commerce customer churn prediction by gradient boosted trees. In: 2020 10th international conference on computer and knowledge engineering (ICCKE). IEEE, pp 55–59
2. Lalwani P, Mishra MK, Chadha JS, Sethi P (2022) Customer churn prediction system: a machine learning approach. Computing 104(2):271–294
3. Abbasimehr H, Setak M, Tarokh MJ (2014) A comparative assessment of the performance of ensemble learning in customer churn prediction. Int Arab J Inf Technol 11(6):599–606
4. Sudharsan R, Ganesh EN (2022) A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy. Connect Sci 34(1):1855–1876
5. Fathian M, Hoseinpoor Y, Minaei-Bidgoli B (2016) Offering a hybrid approach of data mining to predict the customer churn based on bagging and boosting methods. Kybernetes 45(5):732–743