___

• Başoğlu Kabran, F., Ünlü K. D., A two-step machine learning approach to predict S&P 500 bubbles, Journal of Applied Statistics, (2020), 1-19. https://doi.org/10.1080/02664763.2020.1823947
• Huang, W., Nakamori, Y., Wang, S. Y., Forecasting stock market movement direction with support vector machine, Computers & Operations Research, 32(10) (2005), 2513-2522. https://doi.org/10.1016/j.cor.2004.03.016
• Tay, F. E., Cao, L., Application of support vector machines in financial time series forecasting, Omega, 29(4) (2001), 309-317. https://doi.org/10.1016/S0305-0483(01)00026-3
• Xie, W., Yu, L., Xu, S., Wang, S., A new method for crude oil price forecasting based on support vector machines, International conference on computational science, (2006) 44-451. Springer, Berlin, Heidelberg.
• Lu, W. Z., Wang, W. J., Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends, Chemosphere, 59(5) (2005), 693-701. https://doi.org/10.1016/j.chemosphere.2004.10.032
• De Caigny, A., Coussement, K., De Bock, K. W., Lessmann, S., Incorporating textual information in customer churn prediction models based on a convolutional neural network, International Journal of Forecasting, 36(4) (2020), 1563-1578. https://doi.org/10.1016/j.ijforecast.2019.03.029
• Qu, Z., Wang, W., Qu, N., Liu, Y., Lv, H., Hu, K., Song, J. A , Forecasting method of electricity sales considering the user churn rate in a power market environment, Journal of Electrical Engineering & Technology, 14(4) (2019), 1585-1596. https://doi.org/10.1007/s42835-019-00215-9
• Nie, G., Rowe, W., Zhang, L., Tian, Y.,Shi, Y., Credit card churn forecasting by logistic regression and decision tree. Expert Systems with Applications, 38(12) (2011), 15273-15285. https://doi.org/10.1016/j.eswa.2011.06.028
• Yu, X., Guo, S., Guo, J., Huang, X., An extended support vector machine forecasting framework for customer churn in e-commerce, Expert Systems with Applications, 38(3) (2011), 1425-1430. https://doi.org/10.1016/j.eswa.2010.07.049
• Andrews, R., Zacharias, R., Antony, S.,James, M. M., Churn prediction in telecom sector using machine learning, International Journal of Information, 8(2) (2019), https://doi.org/10.30534/ijiscs/2019/31822019
• Sabbeh, S. F., Machine-learning techniques for customer retention: A comparative study, International Journal of Advanced Computer Science and Applications, 9(2) (2018).
• Pamina, J., Raja, B., SathyaBama, S., Sruthi, M. S., VJ, A., An effective classifier for predicting churn in telecommunication, Journal of Adv. Research in Dynamical & Control Systems, 11 (2019).
• Vo, N. N., Liu, S., Li, X., Xu, G. . Leveraging unstructured call log data for customer churn prediction, Knowledge-Based Systems, 212 (2021), 106586, https://doi.org/10.1016/j.knosys.2020.106586
• Lalwani, P., Mishra, M. K., Chadha, J. S., Sethi, P., Customer churn prediction system: a machine learning approach, Computing, (2021), 1-24, https://doi.org/10.1007/s00607-021- 00908-y
• Zhuang, Y., Research on E-commerce customer churn prediction based on improved value model and XG-boost algorithm, Management Science and Engineering, 12(3) (2018), 51-56.
• Cortes, C., Vapnik, V., Support-vector networks. Machine learning, 20(3) (1995), 273-297.
• Snoek, J., Larochelle, H., Adams, R. P., Practical Bayesian optimization of machine learning algorithms. arXiv preprint, (2012), arXiv:1206.2944.
• Injadat, M., Salo, F., Nassif, A. B., Essex, A., Shami, A., Bayesian optimization with machine learning algorithms towards anomaly detection. IEEE global communications conference, (2018), 1-6.
• Yang, L., Shami, A. On hyperparameter optimization of machine learning algorithms: Theory and practice, Neurocomputing, 415 (2020), 295-316. https://doi.org/10.1016/j.neucom.2020.07.061
• Kaggle https://www.kaggle.com/sakshigoyal7/credit-card-customers, Acceded:January 3, 2021.