Prediction of the Reasons for Losing Credit Card Customers based on Multiple Models

Abstract

The data in this article is taken from Kaggle, and the data is publicly available. The data mainly focuses on the nearly 20 different possible reasons that the bank is losing credit card customers, such as their monthly and yearly income or gender. However, some of the data in the original dataset is embodied in words. For this kind of data, the data needs to be converted into numbers and then put into the model for testing. It is also important to check the data whether it has the outliers. Putting the data set into the models to test the accuracy. By sorting the results of different models, the model with the best matching degree is finally obtained. Then, the highly correlated factors in the model are sorted to get the main reasons why banks lose credit card customers. In the end, providing some business advice for banks could be used to identify the same group of credit card customers, and providing some advice to banks could be used to save credit card customers.