A model based on random forest algorithm and Jaya optimization to predict bank customer churn

Customer churn is a financial term that refers to the loss of a customer; Today, due the large number of banks , the loss of customers from one bank to another has become a serious concern for different banks. Therefore, in this article, which has been compiled for the customers of a bank , it is possible to identify customers who have a high probability of falling by considering the behavior and characteristics of the customers before the fall occurs and to keep them by providing suggestions. In marketing, everyone agrees that keeping a customer is much less expensive than attracting a new customer, this article introduces the different phases of the approach of predicting customer churn with the help of machine learning. The proposed method is a combination of random forest algorithms and Jaya optimization, and customer dropout is based on different characteristics. Customer like age, Gender, graphs and cases It predicts more . The results of model in the article are 91.41%, 95.66% and 93.35% respectively in Precision , Recall and Accuracy criteria.