A Hybrid Machine Learning Model for Bank Customer Churn Prediction

A Hybrid Machine Learning Model for Bank Customer Churn Prediction
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-6
Year of Publication : 2022
Authors : Shawni Dutta, Payal Bose, Samir Kumar Bandyopadhyay, Midhunchakkaravarthy Janarthanan
DOI : 10.14445/22315381/IJETT-V70I6P202

How to Cite?

Shawni Dutta, Payal Bose, Samir Kumar Bandyopadhyay, Midhunchakkaravarthy Janarthanan, "A Hybrid Machine Learning Model for Bank Customer Churn Prediction," International Journal of Engineering Trends and Technology, vol. 70, no. 6, pp. 13-23, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I6P202

Abstract
Customer retention often plays an utmost important role for any organization to ensure better profitability. Recently, business organizations are moving toward an automated information-driven decision-making process. This study facilitates the development of a decision-making system that will gather knowledge from customer databases and segregate the customers who are likely to leave the organization. A hybrid machine learning model has been proposed in this study that will boost predictive performance. Two sets of machine learning-based models, namely; single learners and ensemble learners, are applied to the customer retention database. The models are implemented by adjusting underlying parameters to infer the best predictive performance. Finally, the best model from each category is picked up and assembled to construct the hybrid model. From single learner and ensemble-based models, SVM and Adaboost turn out to be promising models, respectively. Hence, the SVM and Adaboost models are unified under a single platform, which significantly outperforms other pre-existing models. The proposed hybrid model (SVM-Adaboost) can provide informed decisions to the business organizations regarding the customer retention strategy with an efficiency of 87%

Keywords
Support Vector Machines (SVM), AdaBoost, SVM-Adaboost, Churn prediction, Machine learning (ML).

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