TY  - JOUR
T1  - Predicting credit card customer churn using support vector machine based on Bayesian optimization
AU  - Ünlü, Kamil Demirberk
PY  - 2021
DA  - December
Y2  - 2021
DO  - 10.31801/cfsuasmas.899206
JF  - Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics
JO  - Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat.
PB  - Ankara University
WT  - DergiPark
SN  - 1303-5991
SP  - 827
EP  - 836
VL  - 70
IS  - 2
LA  - en
AB  - 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 accuracyof %84.
KW  - Churn analysis
KW  - support vector machine
KW  - machine learning
KW  - hyper-parameter optimization
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UR  - https://doi.org/10.31801/cfsuasmas.899206
L1  - https://dergipark.org.tr/en/download/article-file/1646899
ER  -