References
[1]. Garcia, S., Luengo, J., & Herrera, F. (2019). Data preprocessing in data mining. Springer.
[2]. Lopez, P., & Gonzalez, A. (2020). Addressing class imbalance in financial datasets. Journal of Financial Data Science, 5(2), 123-140.
[3]. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Springer.
[4]. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
[5]. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.
[6]. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
[7]. Zhang, Y., & Wang, Q. (2023). Adaptive Ensemble Learning for Imbalanced Financial Data. IEEE Transactions on Knowledge and Data Engineering, 35(4), 1456-1470.
[8]. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357.
[9]. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).
Cite this article
Jin,H. (2025). Bank Customer Churn Prediction Based on Machine Learning Models. Advances in Economics, Management and Political Sciences,170,38-48.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Garcia, S., Luengo, J., & Herrera, F. (2019). Data preprocessing in data mining. Springer.
[2]. Lopez, P., & Gonzalez, A. (2020). Addressing class imbalance in financial datasets. Journal of Financial Data Science, 5(2), 123-140.
[3]. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning. Springer.
[4]. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
[5]. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.
[6]. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
[7]. Zhang, Y., & Wang, Q. (2023). Adaptive Ensemble Learning for Imbalanced Financial Data. IEEE Transactions on Knowledge and Data Engineering, 35(4), 1456-1470.
[8]. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321-357.
[9]. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785-794).