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

Year 2021, Volume: 70 Issue: 2, 827 - 836, 31.12.2021

Kamil Demirberk Ünlü

https://doi.org/10.31801/cfsuasmas.899206

Cited By: 3

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.

Keywords

Churn analysis, support vector machine, machine learning, hyper-parameter optimization

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