Machine learning based churn prediction in telecom

Abstract

In the new era of continuous social development, the level of information technology service construction is also increasing, among which the telecommunications service industry has also developed into a relatively large business cluster under this trend. Due to fierce commercial competition, telecommunication companies also face the problem of losing customers. Asking telecom companies to determine the trend of customer churn from the huge amount of customer information data has become a challenge for all telecom companies to solve. This paper takes a telecom company as a reference, using a large amount of subscriber data from it as a research sample, and uses data mining machine learning and other techniques to make churn predictions on customer data, using three classification methods to evaluate the prediction results and help telecom service providers analyze the causes of customer churn. Firstly, the situation faced by the telecom service company is discussed in terms of the relevant background, then by presenting and analyzing the customer data, and finally by using Logical Regression, Mission Tree, and SVM classification models for classification and evaluation, the causes associated with customer churn are summarized, then the methods of customer churn retention are outlined, and the factors associated with customer churn are listed. The factors associated with churn are listed. This provides a reference for an in-depth study of the theoretical approach and application of customer churn.