Application of the RFA-XGBoost model in predicting potential complaint users in mobile network

Abstract

In order to predict and reduce the occurrence of complaints of mobile network users in advance, the application of multidimensional data analysis in the prediction of potential complaints of mobile network users was deeply studied. By collecting a wide range of business domain and operation domain indicators of mobile network users as input feature data, a potential complaint user prediction model based on extreme gradient boosting (XGBoost) was successfully constructed, which had an average prediction accuracy of 96.35% on the test set. At the same time, the recursive feature augmented XGBoost (RFA-XGBoost) prediction model was proposed for the prediction of potential complaint users. By iteratively adding the predicted output of the previous round of XGBoost model to the feature set as a new feature and retraining the new round of XGBoost model, the average prediction accuracy after optimization could be improved to 98.89%. For mobile network operators, the research results mean that they can identify and intervene in potential complaints earlier, so as to effectively prevent the occurrence of complaints and further improve user satisfaction and service quality, which has important practical significance and commercial value.

Keywords

• prediction of potential complaint users
• machine learning
• XGBoost
• data mining