A Hybrid Machine Learning Model for Bank Customer Churn Prediction
A Hybrid Machine Learning Model for Bank Customer Churn Prediction
|© 2022 by IJETT Journal|
|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
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%
Support Vector Machines (SVM), AdaBoost, SVM-Adaboost, Churn prediction, Machine learning (ML).
 Ahn J. H, Han S. P & Lee Y. S, Customer Churn Analysis: Churn Determinants and Mediation Effects of Partial Defection in the Korean Mobile Telecommunications Service Industry, Telecommunications Policy. 30(10-11) (2006) 552-568.
 Lee J, Lee J & Feick L, The Impact of Switching Costs on the Customer Satisfaction, Loyalty Link: Mobile Phone Service in France, Journal of Services Marketing. 15(1) (2001) 35-48.
 Badillo S, Bánfai B, Birzele F, Davydov I.I, Hutchinson L, Kam-Thong T, Siebourg-Polster J, Steiert B, Zhang J.D, An Introduction to Machine Learning, Clinical Pharmacology and Therapeutics. 107 (2020) 871 - 885.
 Jayakumar Sadhasivam, Arpit Rathore, Indrajit Bose, Soumya Bhattacharjee, Senthil Jayavel, A Survey of Machine Learning Algorithms International Journal of Engineering Trends and Technology. 68(4) (2020) 64-71.
 Rani K. S, Shaik T, Prasanna N.G.L, Vindhya R, Srilakshmi P, Analysis of Customer Churn Prediction in Telecom Industry Using Logistic Regression. International Journal of Innovative Research in Computer Science & Technology IJIRCST. 9(4) (2021). https://doi.org/10.21276/ijircst.2021.9.4.6.
 Lalwani P, Mishra M. K, Chadha J. S, Sethi P, Customer Churn Prediction System: A Machine Learning Approach Computing. 104(2) (2022) 271-294.
 Naidu G, Zuva T, Sibanda E.M, Systematic Review on Churn Prediction Systems in Telecommunications, In Bindhu V, Tavares J.M.R.S, Du KL, eds., Proceedings of Third International Conference on Communication, Computing and Electronics Systems, Lecture Notes in Electrical Engineering Springer, Singapore. 844 (2022). https://doi.org/10.1007/978-981-16-8862-1_64.
 Pustokhina I. V, Pustokhin D. A, Aswathy R. H, Jayasankar T, Jeyalakshmi C, Díaz V. G Shankar K, Dynamic Customer Churn Prediction Strategy for Business Intelligence Using Text Analytics with Evolutionary Optimization Algorithms, Information Processing & Management. 58(6) (2021) 102706.
 Kavita M, Sharma N & Aggarwal G, Churn Prediction of Customer in Telecommunication and E-Commerce Industry Using Machine Learning, PalArch's Journal of Archaeology of Egypt/Egyptology. 17(9) (2020) 136-145.
 Rahman M & Kumar V, Machine Learning Based Customer Churn Prediction in Banking, In 2020 4th International Conference on Electronics, Communication and Aerospace Technology, IEEE ICECA. (2020) 1196-1201.
 Kavitha V, Kumar G. H, Kumar S. M & Harish M, Churn Prediction of Customer in Telecom Industry Using Machine Learning Algorithms, Int. J. Eng. Res. Technol, IJERT. 9(5) (2020) 181-184.
 Abou el Kassem E, Hussein S. A, Abdelrahman A. M & Alsheref F. K, Customer Churn Prediction Model and Identifying Features to Increase Customer Retention based on User Generated Content, International Journal of Advanced Computer Science and Applications. 11(5) (2020) 522-527.
 Ahmad A. K, Jafar A & Aljoumaa K, Customer Churn Prediction in Telecom Using Machine Learning in Big Data Platform, Journal of Big Data. 6(1) (2019) 1-24.
 Saran Kumar A & Chandrakala D, A Survey on Customer Churn Prediction Using Machine Learning Techniques, International Journal of Computer Applications. 975 (2016) 8887.
 Hang C & Ma Y, Eds., Ensemble Machine Learning: Methods and Applications, Springer Science & Business Media. (2012) 1-34.
 Suthaharan S, Support Vector Machine. Machine Learning Models and Algorithms for Big Data Classification, Springer, Boston, MA. (2016) 207-235.
 Zhang Z, Introduction to Machine Learning: k-Nearest Neighbors, Annals of Translational Medicine. 4(11) (2016) 218.
 Berrar D, Bayes’ Theorem and Naive Bayes Classifier, Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics. 403 (2018) 1-18.
 Priyam A, Abhijeeta G. R, Rathee A & Srivastava S, Comparatanalive Ysis of Decision Tree Classification Algorithms, International Journal of Current Engineering and Technology. 3(2) (2013) 334-337.
 Schapire R. E, Explaining Adaboost, Empirical Inference, Springer, Berlin, Heidelberg. (2013) 37-52.
 Natekin A & Knoll A, Gradient Boosting Machines, A Tutorial, Frontiers in Neurorobotics. 7 (2013) 21.
 Livingston F, Implementation of Breiman’s Random Forest Machine Learning Algorithm, ECE591Q Machine Learning Journal Paper. (2005) 1-13.
 (2022). The Kaggle Website [Online] Available: https://www.kaggle.com/code/kmalit/bank-customer-churn-prediction/data
 Naimi A. I & Balzer L. B, Stacked Generalization: An Introduction to Super Learning, European journal of Epidemiology. 33(5) (2018) 459-464.
 Jyoti Goyal, Bal Kishan, TLHEL: Two Layer Heterogeneous Ensemble Learning for Prediction of Software Faults International Journal of Engineering Trends and Technology. 69(4) 2021 16-20