Research on Application of Stacking Technique in Telecom Churn Prediction

Abstract

In today's saturated telecommunication market, operators need to fully tap the value of data, strengthen the management of existing users, and reduce customer churns. In order to further to reduce the maintenance cost of enterprises, it is necessary to apply more efficient data mining technology to the prediction of telecommunication user churns in order to improve the operating profit of enterprises. This paper took a telecommunication customer churn data set on Kaggle as the research data, and explores a high accuracy customer churn prediction model. Logistic Regression was exploited to create customer churn prediction models, and the parameters of each model were optimized. On this basis, the Stacking technique was used to fuse the models, and the best combination method is obtained. After research, this paper mainly drew the following conclusions: First, in a single model, the model built with XG Boost algorithm has the highest prediction accuracy, which is 86.20%. Secondly, combining the accuracy rate of training set, cross-validation set and test set, it can be found that XG Boost and Random Forest have the best performance under Stacking fusion algorithm. The accuracy of the above three datasets is 97.61%, 85.84%, 86.51%, respectively, which improves the prediction accuracy of the prediction model.