EAS Publisher | A Comparative Study of Machine Learning Algorithms for Bank Customer Churn Prediction

A Comparative Study of Machine Learning Algorithms for Bank Customer Churn Prediction

DOI : https://doi.org/10.36349/easjecs.2024.v07i01.001

1823 Downloads 5 Views

Bank churn, or the phenomenon of customers leaving their bank for another, is a major concern for financial institutions. The ability to predict churn can help banks to retain customers, reduce costs, and increase profitability. In this paper, we propose a machine learning approach to predict bank churn using a dataset of customer transactions and demographic information. We compare the performance of different machine learning models, including logistic regression, decision tree, random forest, and neural network, using evaluation metrics such as accuracy, precision, recall, and F1-score. Our results show that the random forest model outperforms other models with an accuracy of 87.5% and an F1-score of 0.87. Furthermore, we conduct feature selection and engineering to identify the most important features contributing to bank churn. Our findings suggest that factors such as customer age, account balance, and number of transactions are significant predictors of churn. Our research has important implications for banks to improve customer retention and reduce churn by leveraging machine learning techniques.

TOP EDITORS

Dr. T. Selvankumar
Chief Editor
EAS Journal of Biotechnology and Genetics

Dr. James Kay, PhD
Chief Editor
EAS Journal of Psychology and Behavioural Sciences

Dr. Rejeesh Menon
Chief Editor
EAS Journal of Medicine and Surgery

Dr. S. Jayachandran
Chief Editor
EAS Journal of Dentistry and Oral Medicine

Dr. Md. Habibur Rahman
Chief Editor
EAS Journal of Pharmacy and Pharmacology

Dr. Benard Chemwei, PhD
Chief Editor
East African Scholars Multidisciplinary Bulletin

NFI Joseph Lon
Chief Editor
EAS Journal of Humanities and Cultural Studies

Prof. Dr. Nazir Ahmad Suhail
Chief Editor
East African Scholar Journal of Engineering and Computer Sciences

Dr. Hamid Osman Hamid
Chief Editor
EAS Journals of Radiology and Imaging Technology

Dr. BOUCENNA Mounir
Chief Editor
EAS Journal of Veterinary Medical Science

OPEN ACCESS JOURNALS

Dr. Afroza Begum

Lecturer, Dept. of Pharmacology and Therapeutics, Shaheed Monsur Ali Medical College & Hospital, Uttara, Dhaka-1230, Bangladesh

BEST AUTHOR

Of The Month

TRACK YOUR ARTICLE

Enter the Manuscript Reference Number (MRN)

Get Details