Customer churn prediction plays an important role in customer relationship management. To do so, classification algorithms are powerful tools to predict the churner customers in the real world. In this paper, the customer churn prediction is considered as a binary classification problem. The aim of this paper is to apply an ensemble approach based on bagging algorithm for customer churn prediction. It is demonstrated that a bagging approach for base classifiers can results better prediction performance. The proposed approaches are applied to a real dataset to illustrate the Bagging effectiveness. The results are compared with other base classifiers.

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