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

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dc.contributor.authorÜnlü, Kamil Demirberk
dc.contributor.departmentOthertr_TR
dc.contributor.facultyOthertr_TR
dc.date.accessioned2022-12-21T11:00:47Z
dc.date.available2022-12-21T11:00:47Z
dc.date.issued2022
dc.description.abstractIn 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.tr_TR
dc.description.indexTrdizintr_TR
dc.identifier.endpage836tr_TR
dc.identifier.issn/e-issn1303-5991
dc.identifier.issue2tr_TR
dc.identifier.startpage827tr_TR
dc.identifier.urihttps://doi.org/10.31801/cfsuasmas.899206tr_TR
dc.identifier.urihttp://hdl.handle.net/20.500.12575/86291
dc.identifier.volume70tr_TR
dc.language.isoentr_TR
dc.publisherAnkara Üniversitesitr_TR
dc.relation.isversionof10.31801/cfsuasmas.899206tr_TR
dc.relation.journalCommunications, Series A1:Mathematics and Statisticstr_TR
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarıtr_TR
dc.subjectChurn analysis, support vector machine, machine learning, hyper-parameter optimizationtr_TR
dc.titlePredicting credit card customer churn using support vector machine based on Bayesian optimizationtr_TR
dc.typeArticletr_TR

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Cilt:70 Sayı:02 (2021)