Analysis And Prediction Of User Churn Based On Data Mining

As mobile Internet technology is becoming more mature,the proportion of users has increased.The online hotel reservation market now is really huge.The user growth rate of Internet products has slowed down,and the growth value is close to the ceiling.How to improve user retention and reduce loss is imminent.Data mining is an important means to obtain valuable information from data.It is of great significance to use data mining technology to study the user data of online travel booking service companies.User churn prediction modeling can accurately predict user churn tendency and formulate user retention strategies in a timely manner.The user churn problem is actually a two-classification problem.Through the research of the current general classification algorithms,this paper selects GBDT and XGBOOST with good classification effects as the classification models,and further improves the performance through model stacking,and comprehensively compares various models.For the impact of missing values,data imbalance,and complex variables on the final model results,this paper uses different methods to fill missing values for different types of variables,uses Borderline-SMOTE sampling method to deal with imbalances,and performs feature engineering on each variable Process to get better results and use multiple methods to rank variable importance.Based on the RFM model,the user portrait system is constructed and corresponding suggestions are made for different types of users.The data preprocessing can effectively improve the accuracy of predicting user churn.It can be found that user activity,order fill-in volume and conversion rate are the key factors that affect whether users are churn.Classify users based on indicators and make recommendations for different categories of users.Through data preprocessing and optimization of model parameters,various evaluation indicators of the original model are improved to make the prediction results more accurate.This article has enriched user analysis and research in the online hotel reservation industry to a certain extent,and looks forward to providing a certain sense of reference for user churn prediction and user life cycle management.