Telecom Customer Churn Prediction And Analysis Based On Improved Random Forest Algorithm

In recent years,the development of the telecommunications industry has been very rapid.In 2019,the number of Internet users nationwide has reached 900 million,and the number of mobile phone users has reached 1.5 billion.The telecommunications market has become saturated.In the era of 5G communications,with the implementation of number portability,telecom operators are trying to maintain their strength in the market and avoid customer churn trying to switch telecom partners.Therefore,the prediction of churn of telecom customers is very important for telecom companies to maintain and retain users.Through the analysis of telecommunication customer churn prediction problems,it is pointed out that the key factors for constructing telecommunication customer churn models are business understanding and data mining algorithm selection.In recent years,various types of machine learning algorithms have been widely used in telecom enterprise data mining practices.By implementing traditional machine learning algorithms,it is pointed out that in addition to standardized input data,choosing the appropriate data mining method can significantly improve telecommunications customer churn prediction.Success rate.Comprehensive comparison,in dealing with the classification problem of unbalanced data sets,the classification effect of the random forest algorithm in the traditional algorithm is superior to other algorithms.The Kmeans-smote fusion sampling is used in the sampling stage of the data set,and the f1 value is increased by 3%on average compared with other sampling methods.A new elastic network method is adopted for feature selection.The improved model has an increase of 5%compared with the previous AUC value.At the algorithm level,this paper merges the clustering algorithm into the random forest algorithm to construct a new random forest model,in which the clustering algorithm is used to select the generator subtree of the random forest.Experiments prove the excellent characteristics of the improved algorithm.The data in this topic uses Southeast Asian telecommunications enterprise customer and behavior data.It mainly studies high-value customers,finds the causes of customer churn through feature selection,draws a multi-index evaluation chart,and more intuitively shows the probability and score of customer churn.The prediction research results of this article will provide valuable reference information for telecom companies and improve marketing efficiency.