The worst performance achieved by sigmoid kernel which has accuracy of %84.
📌 3 Key Takeaways
🎯
Objective & Hypothesis:In this study, we have employed a hybrid machine learning algorithm to predict customer credit card churn.
📊
Key Findings & Data:Used metrics are accuracy, precision, recall and F1-score.
💡
Conclusion & Impact:The worst performance achieved by sigmoid kernel which has accuracy of %84.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Churn analysis · support vector machine · machine learning
Abstract
In this study, we have employed a hybrid machine learning algorithm to predict customer credit card churn. The proposed model is Support Vector Machine (SVM) with Bayesian Optimization (BO). BO is used to optimize the hyper-parameters of the SVM. Four different kernels are utilized. The hyper-parameters of the utilized kernels are calculated by the BO. The prediction power of the proposed models are compared by four different evaluation metrics. Used metrics are accuracy, precision, recall and F1-score. According to each metrics linear kernel has the highest performance. It has accuracy of %91. The worst performance achieved by sigmoid kernel which has accuracy of %84.
ÜNLÜ, K. D. (2021). Predicting credit card customer churn using support vector machine based on Bayesian optimization. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, Cilt: 70 - Sayı: 2, 827–836. https://ajindex.com/articles/746703
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
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