Research On Telecom Customer Churn Based On Machine Learning

In the past decade,the telecommunications industry has been rapidly developing.Customer churn has always been an important challenge for telecom companies,as it not only leads to a decrease in revenue,but also affects customer loyalty and brand reputation.To achieve greater economic benefits while maintaining normal business operations,it is crucial to maintain a good relationship between customers and the enterprise.Customers are the core of telecom services,as they are related to the revenue sources,word-of-mouth promotion,and acquisition of new customers for telecom companies.According to research,the cost of retaining an existing customer in the telecom industry is lower than the cost of acquiring a new one.Therefore,from an economic perspective,it is necessary to analyze customer churn in the telecommunications industry.This article focuses on using machine learning to predict telecom customer churn and proposing targeted strategies to retain more customers.Various machine learning models are compared,and the best-performing model is selected for customer churn prediction.The article first introduces the principles of the applied machine learning models and presents flowcharts of relevant algorithms to facilitate understanding of the models.Large amounts of telecom customer data are collected and analyzed,although there are challenges due to outliers caused by registration or statistical errors,redundant variables,and high data dimensions.Next,the article performs data cleaning before modeling.Preprocessing the data reduces modeling difficulty and can effectively improve model accuracy.The missing and duplicate values are dealt with,and after analyzing categorical variables,variables such as "customer ID" and "geographic region" are found to have no significant impact on customer churn.Variables that affect customer churn are transformed into numerical variables using one-hot encoding for subsequent modeling analysis.Furthermore,the article uses machine learning techniques to predict telecom customer churn and identify key factors affecting churn.Specifically,decision trees,logistic regression,random forests,and Ada Boost algorithms are used to train and test the models.The results of the modeling are visualized and compared horizontally using evaluation metrics such as accuracy,precision,recall,F1 score,and AUC.Additionally,the models are fused using Voting and Stacking algorithms to establish a new model.In contrast to previous studies using a single learner as the base learner,this study innovatively uses the Stacking algorithm to fuse two-layer learners,using a well-performing ensemble learning model as the base learner and a stable logistic regression model as the meta-learner,providing a new approach for telecom customer churn prediction.Logistic regression has an accuracy of 0.5860,precision of 0.5714,recall of0.5945,and F1 score of 0.5827.The Voting algorithm fused model has an accuracy of0.6386,precision of 0.6289,recall of 0.6267,and F1 score of 0.6278.The Stacking algorithm fused model has an accuracy of 0.7433,precision of 0.7587,recall of0.7460,and F1 score of 0.7523.The Stacking algorithm performs the best in predicting telecom customer churn,with increases in accuracy,precision,recall,and F1 score of 26.8%,32.8%,25.5%,and 29.1%,respectively,compared to the logistic model.In conclusion,the article finds that the Stacking algorithm fused model is significantly better than other models in predicting telecom customer churn and can be applied to research on telecom customer churn.