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 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.
Highlights
Customer churn is a business term expression which describes loss of customers
The results show that using textual data as a feature of the model increases the performance of the proposed model. [7] use churn rate of the customer to predict the electricity sales of the power market
It is noted that the new algorithm handles data well when imbalanced is an issue. [10] propose Support Vector Machine (SVM) and random forest (RF) to predict customer churn of telecom sector and the results reveal that the investigate learning models behave
Summary
Customer churn is a business term expression which describes loss of customers. Firms invest in order not to lose their customers. Marketing departments continuously investigate the behavior of their existing customers and potential customers to understand the underlying causes of churn. These investigations are costly and time consuming. We utilized SVM as the classi...er in this study because it ensure to use the technique called kernel transformations, projects the features space to a higher dimension, which makes it easier to ...nd the bound between the classi...cation objects. These kernels are non-linear so SVM can
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