Bank Churn Prediction: A Holistic Churn Analysis on Imbalanced Data Using ML Techniques
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-2550-2_32
Reference22 articles.
1. Chouiekh A, Haj EHIE (2020) Deep convolutional neural networks for customer churn prediction analysis. Int J Cognit Inf Nat Intell 14(1):1–16. https://doi.org/10.4018/ijcini.2020010101
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3. Lee N-T, Lee H-C, Hsin J, Fang S-H (2024) Prediction of customer behavior changing via a hybrid approach. IEEE Open J Comput Soc 5:27–38. https://doi.org/10.1109/OJCS.2023.3336904
4. Brandusoiu I, Toderean G, Beleiu H (2016) Methods for churn prediction in the pre-paid mobile telecommunications industry. pp 97–100. https://doi.org/10.1109/ICComm.2016.7528311
5. Amin A, Anwar S, Adnan A, Nawaz M, Howard N, Qadir J, Hawalah A, Hussain A (2016) Comparing oversampling techniques to handle the class imbalance problem: a customer churn prediction case study. IEEE Access. https://doi.org/10.1109/ACCESS.2016.2619719
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