1246 923
Full Length Article
American Journal of Business and Operations Research
Volume 5 , Issue 1, PP: 21-30 , 2021 | Cite this article as | XML |PDF

Title

Classification Models for Bank Marketing Campaign: Towards Smart Bank Marketing

  Ahmad Freij 1 *

1  American University in the Emirates, Dubai, UAE
    (Afreij790@gmail.com)


Doi   :   https://doi.org/10.54216/AJBOR.050102

Received: April 22, 2021 Accepted: September 14, 2021

Abstract :

In this paper, we have proposed two models of marketing classification which are Support Vector Machine (SVM) and Linear regression, these two models are the most popular and useful models of classification. In this paper, we represent how these two models are used for a case study of a bank marketing campaign, the dataset is related to a bank marketing campaign, and for Applying the machine learning models of classification, the RapidMiner software was used.

Keywords :

Bank Marketing , Machine Learning , Artificial Intelligence , Smart E-Banking , Business Intelligence , Classification , E-Marketing.

References :

[1] Sammut, C., & Webb, G. I. (Eds.). (2011). Encyclopedia of machine learning. Springer Science & Business Media.

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[4] Mitchell, T. M. (1999). Machine learning and data mining. Communications of the ACM, 42(11), 30-36.

[5] Srinivasan, K., & Fisher, D. (1995). Machine learning approaches to estimating software development effort. IEEE Transactions on Software Engineering, 21(2), 126-137.

[6] M. S. Acharya, A. Armaan, and A. S. Antony, "A comparison of regression models for prediction of graduate admissions," in 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), 2019, pp. 1-5.

[7] Z. Zhang, Y. Li, L. Li, Z. Li, and S. Liu, "Multiple linear regression for high-efficiency video intra coding," in ICASSP 2019-2019 IEEE

 

[8] A. K. Prasad, M. Ahadi, B. S. Thakur, and S. Roy, "Accurate polynomial chaos expansion for variability analysis using optimal design of experiments," in 2015 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO), 2015, pp. 1-4

 

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[10] T. Bakibayev and A. Kulzhanova, "Common Movement Prediction using Polynomial Regression," in 2018 IEEE 12th International Conference on Application of Information and Communication Technologies (AICT), 2018, pp. 1-4


Cite this Article as :
Style #
MLA Ahmad Freij. "Classification Models for Bank Marketing Campaign: Towards Smart Bank Marketing." American Journal of Business and Operations Research, Vol. 5, No. 1, 2021 ,PP. 21-30 (Doi   :  https://doi.org/10.54216/AJBOR.050102)
APA Ahmad Freij. (2021). Classification Models for Bank Marketing Campaign: Towards Smart Bank Marketing. Journal of American Journal of Business and Operations Research, 5 ( 1 ), 21-30 (Doi   :  https://doi.org/10.54216/AJBOR.050102)
Chicago Ahmad Freij. "Classification Models for Bank Marketing Campaign: Towards Smart Bank Marketing." Journal of American Journal of Business and Operations Research, 5 no. 1 (2021): 21-30 (Doi   :  https://doi.org/10.54216/AJBOR.050102)
Harvard Ahmad Freij. (2021). Classification Models for Bank Marketing Campaign: Towards Smart Bank Marketing. Journal of American Journal of Business and Operations Research, 5 ( 1 ), 21-30 (Doi   :  https://doi.org/10.54216/AJBOR.050102)
Vancouver Ahmad Freij. Classification Models for Bank Marketing Campaign: Towards Smart Bank Marketing. Journal of American Journal of Business and Operations Research, (2021); 5 ( 1 ): 21-30 (Doi   :  https://doi.org/10.54216/AJBOR.050102)
IEEE Ahmad Freij, Classification Models for Bank Marketing Campaign: Towards Smart Bank Marketing, Journal of American Journal of Business and Operations Research, Vol. 5 , No. 1 , (2021) : 21-30 (Doi   :  https://doi.org/10.54216/AJBOR.050102)