Abstract
In order to solve the problem that the nonlinear information of data in the field of telecom customer churn prediction is not fully used, or even ignored, which leads to inaccurate prediction, this paper introduces the mutual information feature selection method (MIPCA) to filter the features and reduce the dimensions of customer data, and proposes an XGBoost method based on the mutual information feature selection method(MIPCA-XGBoost), which improves the accuracy of the prediction results. By using the data set of telecom industry customers published on Kaggle website, compares the prediction result of this method with that of machine learning algorithms commonly used in this field, and proves the accuracy, recall and F_Score of MIPCA-XGBoost method is higher than other algorithms.
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