Research On Bank Customer Churn Model Based On Data Mining Technolog

With the development of commercial society,customer resources have become the main goal of competition in various industries.Especially in the context of sluggish growth in the total number of customers,preventing customer churn has become as important as searching for new customers.At the business level,banks can rely on high-quality investment projects to improve product yields and retain important customers.At the technical level,banks can fully utilize customer information,explore customer behavior logic,and prevent potential customer churn.In this regard,there are RFM models based on traditional statistics and Customer Lifetime Value models.Data mining technology can explore hidden patterns within data and conduct matrix iterative training based on customer characteristics.This can greatly improve the efficiency and accuracy of customer churn prediction.This also helps banking entities better take effective retention measures,thereby reducing the overall rate of bank customer churn.The main work of this article is as follows:(1)This paper studies the processing flow of bank customer dataset,and expounds the basic theories and methods used to process the dataset.The processing methods involved in this article mainly include two aspects: using SMOTE and its derived methods to supplement the original unbalanced dataset with similar data;For the analysis of abnormal and missing values,a comparison of 3 σ Rule,Markov distance,and box graph,and modify the dataset after selecting the best.(2)Analyze customer data sets based on clustering algorithms,establish new features based on the relationship between data features,and rank customer features in importance.Classify customers according to the feature distribution space,discuss the main reasons for customer churn in different groups,and finally propose retention suggestions for them.(3)Based on the decision tree algorithm,the customer dataset is trained,and on this basis,the training process and construction method of the random forest and XGBoost tree models are added.Creatively modify XGBoost model training results through customized loss functions,and repeatedly improve the prediction effects of the three models through cross validation,iterative training,and further improve the accuracy of customer churn models.In the task of predicting bank customer churn,we studied the ROC curves of three algorithms,and then comprehensively evaluated the training effect of the model based on model evaluation indicators such as recall rate,accuracy,AUC,and F score.Finally,we successfully used the XGBoost model to accurately predict customer churn probability.It reflects the significant improvement effect of XGBoost on model performance and accuracy,and proves the effectiveness of prediction.