Customer - Churn Prediction Using Machine Learning

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dc.contributor.authorAgarwal, Varsha
dc.date.accessioned2025-05-19T04:19:39Z
dc.date.available2025-05-19T04:19:39Z
dc.date.issued2022-12-28
dc.descriptionISME
dc.description.abstractThe gradual but consistent decrease in the number of customers retained over time is referred to as “customer churn,” and it is a word that is frequently used in the business and financial sectors. If a company can identify the customers who are most likely to leave, they are more likely to take preventative efforts to keep those customers as clients. It is to the bank's advantage to have knowledge about which customers are theoretically and practically most likely to switch banks in the relatively close future. This article explains how to use machine learning algorithms to identify banking customers who may be considering switching financial institutions. This article demonstrates how machine learning models such as Logistic Regression (LR) and Naive Bayes' (NB) can effectively forecast which customers are most likely to leave the bank in the future by using data such as age, location, gender, credit card information, balance, etc. The article also uses data such as age, location, gender, credit card information, balance, etc. In addition, this article demonstrates the probabilistic predictions that may be generated using machine learning models such as Logistic Regression (LR) and Naive Bayes (NB). The findings of this research ultimately point to the conclusion that NB is superior to LR.
dc.identifier.isbn978-1-6654-7657-7
dc.identifier.urihttps://atlasuniversitylibraryir.in/handle/123456789/723
dc.language.isoen
dc.publisherIEEE
dc.subjectTechnology
dc.subjectComputation Science
dc.titleCustomer - Churn Prediction Using Machine Learning
dc.typeBook chapter

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