Open AccessBank Customer Churn Prediction Using Machine Learning
Author(s) -
S.L.D. Santosh Kumar
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.37467
Subject(s) - customer retention , boosting (machine learning) , business , customer to customer , computer science , logistic regression , customer intelligence , random forest , decision tree , marketing , service (business) , machine learning , service quality
Banking is one of the highly competitive sectors where customer relations is of utmost importance for any bank. Each customer is considered as a customer for life by the banks. The term “Customer Churn” refers to the state in which the customer or the subscriber stops involving in business transactions with a company or a service provider. To deal with this, the paper presents the work done towards predicting the customer churn rate, using machine learning models which will indicate whether a customer will leave the bank or not based on many factors, this in turn will help the bank in knowing which category of customers generally tend to leave the bank. Further the banks can bring in exciting offers so that it can retain its customers. In this predictive process popular models such as logistic regression, decision trees, random forest and other boosting techniques have been used to achieve a decent level of accuracy, for the banks to rely upon.