Research on Customer Churn Prediction Model in Broadcasting and Television Industry Based on Ensemble Learning
In the Internet era,the broadcasting industry faces increasingly fierce competition.Aiming at the problem of low accuracy rate in the current customer churn prediction model,a customer churn prediction model for broadcasting based on integrated learning is proposed.Combining the characteristics of customer data,analyzing the differences in the training principles of different models,and through the Stacking integration framework,fusing multiple single prediction models with complementary advantages to achieve accurate prediction of customer churn.Simulations are conducted on the public dataset to verify the results,and the simulation results are compared with the prediction results of several single models.The results show that the proposed model can effectively improve the prediction accuracy of customer churn,which is helpful for the customer retention work of broadcasting operators.
ensemble learningcustomer churn predictionradio and television
NETL
NSTL
万方数据
