Analysis and Comparison of Forecasting Algorithms for Telecom Customer Churn

Abstract

The integrated algorithm is a highly flexible data analysis and prediction algorithm. In many big data competitions at home and abroad, the winning teams basically adopt the idea of integrated algorithms such as random forest, GBDT, XGBoost and other algorithms. This shows that accuracy of ensemble algorithms is still very advantageous in terms of predictive classification. The main task of this article is to predict the loss of telecom customers. Under the current situation of saturation of the telecom market, how to retain the original customers is the main task of each telecom operator. This article mainly compares the four prediction models on the telecom data set. Predictive performance, the final performance evaluation index also shows that the random forest model and XGBoost model of integrated thought have better predictive models.