Telecom customer churn prediction based on imbalanced data re-sampling method

Customer is a very unstable group. Enterprises, of course, are happy to retain customers. While for enterprises, customer churn is always a rare event, but it is necessary to be paid attention. So the imbalanced data problem will arise in the field of telecom customer churn prediction. In this article, we utilize imbalanced data re-sampling method combines Support Vector Machine (SVM) to solve the imbalanced data problem, poor classification performance. Using the appropriate metrics which are more suitable for imbalanced data sets to evaluate the performance, the datasets are obtained from France telecom operator, Orange Telecom, and UCI. The experimental result proves that our prediction model performs satisfactorily, and it can be effective to predict the telecom customer churn.