Abstract:In order to predict customer churn with large sample data, a customer churn prediction model based on spectral regression was put forward from the perspective of feature expression, which took advantage of the spectral regression to reduce the dimension of feature. On the basis of the original customer features, a distinguishing feature space of low dimension is established by using the manifold dimension reduction based on spectral regression, and then we used the support vector machine to realize the binary classification of customer churn prediction. The model was evaluated on two different data sets of network customers and traditional telecom customers, and compared with different classifiers, different feature reduction or selection methods, the experiment results verify that the model is effective.