Customer churn prediction on kernel principal component analysis feature abstraction

ion method was presented. The prediction model was designed by combining Kernel Principal Component Analysis (KPCA) and Logistic regression. Experimental results of customer churn prediction for a telecommunication carrier show that the proposed method is superior to original attributes and PCA feature abstraction in hit rate, covering rate, accuracy rate and lift coefficient, which indicates that KPCA abstracts nonlinear features of customer data and provides an effective measurement for customer churn prediction.