Model of customer churn prediction on support vector machine

To improve the prediction abilities of machine learning methods, a support vector machine(SVM) on structural risk minimization was applied to customer churn prediction. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding customer churn prediction for home and foreign telecommunication carriers. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for customer churn prediction.