Customer Churn Prediction in Telecom Based on Machine Learning

Abstract

Customer churn, a critical metric in the telecommunications industry, reflects the propensity of customers to discontinue service usage, thereby affecting the revenue and growth of a company. Customer churn is greatly affected by individuals and products, and there is no obvious rule, which greatly increases the difficulty of customer loss prediction. With the development of machine learning, the prediction in telecom churn using machine learning has attracted wide attention from researchers. However, there are great differences between different algorithms, which increases the difficulty of application. Therefore, this paper studies the issue of customer churn prediction from the aspect of algorithm. This paper introduces the common data sets and evaluation indexes of customer churn prediction, and then introduces logistic regression, decision tree, K-nearest neighbor, random forest, Gaussian naive Bayes, XGBoost, gradient enhancement and neural network algorithms. Then, through the experimental analysis, the differences between these models are compared. Finally, the paper summarizes the full text and discusses the future research hotspots.