@article{article_899206, title={Predicting credit card customer churn using support vector machine based on Bayesian optimization}, journal={Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics}, volume={70}, pages={827–836}, year={2021}, DOI={10.31801/cfsuasmas.899206}, author={Ünlü, Kamil Demirberk}, keywords={Churn analysis, support vector machine, machine learning, hyper-parameter optimization}, 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 F <sub>1 </sub>-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.}, number={2}, publisher={Ankara University}