Predicting credit card customer churn using support vector machine based on Bayesian optimization

Abstract

In this study, we have employed a hybrid machine learning algorithm to predict credit card customer 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 di¤erent kernels are utilized. The hyper-parameters of the utilized kernels are calculated by the BO. The prediction power of the proposed models is compared by four di¤erent evaluation metrics. Used metrics are accuracy, precision, recall and F 1-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.