Bank Customer Churn Analysis Based on Machine Learning
In order to accurately predict which customers may terminate their business relationship with a certain bank in the next two years,we constructed a customer churn analysis and prediction model based on machine learning.Compared with traditional logistic regression models,this model has significant advantages in predicting customer churn.We use deep neural networks to predict bank customer churn and comprehensively evaluate the prediction results using indicators such as accuracy,recall,precision,and F-Measure.For customers who are predicted to churn,banks can take targeted support measures to address their dissatisfaction and problems,thereby increasing the likelihood of their continued retention.This predictive model not only helps banks identify potential churn risks in advance,but also provides important opportunities for banks to retain customers and help maintain long-term customer relationships.
NETL
NSTL
万方数据
维普
