1
Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt
(marwa.3eeed@gmail.com)
2
Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt
(skenawy@ieee.org)
3
Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, 35516, Mansoura Egypt
(afai79@mans.edu.eg)
4
Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia;Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
(abdelaziz@cis.asu.edu.eg)
5
Electronics and Communications Engineering Dep., Faculty of Engineering, Delta University for Science and Technology, Gamasa City, Mansoura, Egypt
(mohamed.saber@deltauniv.edu.eg)
Abstract :
Nowadays, the banking industry is no exception to the general trend of massive data production in all spheres of modern life. In this research, we analyze the categorization of marketing data from banks using a variety of machine learning techniques. The term "banking" refers to the supply of services by a bank to an individual consumer. The data was first compiled from the UCI Machine Learning repository and the Kaggle website. Phone-based banking marketing statistics are the focus of this data set. Python is utilized as the language of implementation, and the Machine Learning concept is employed for statistical learning and data analysis in this work. An improved prediction is the primary goal of machine learning's model-building phase. In order to classify the results, a supervised Naive Bayes algorithm is used to the data. The primary goal of the modeling effort is to characterize whether or not the consumer has chosen a term deposit. The bank should devote substantial time to returning phone calls from prospective customers. Accuracy, precision, recall, and F1 score were all evaluated as a consequence of this study in the direction of term deposit forecasting.
Keywords :
Customer; bank marketing; machine learning; machine learning; metaheuristic optimization algorithms
References :
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| Style | # |
|---|---|
| MLA | Marwa M. Eid, El-Sayed M. El-Kenawy, Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid, Mohamed Saber. "Bank Marketing Data Classification Using Optimized Voting Ensemble, Sine Cosine, and Genetic Algorithms." American Journal of Business and Operations Research, Vol. 8, No. 2, 2022 ,PP. 16-24 (Doi : https://doi.org/10.54216/AJBOR.080202) |
| APA | Marwa M. Eid, El-Sayed M. El-Kenawy, Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid, Mohamed Saber. (2022). Bank Marketing Data Classification Using Optimized Voting Ensemble, Sine Cosine, and Genetic Algorithms. Journal of American Journal of Business and Operations Research, 8 ( 2 ), 16-24 (Doi : https://doi.org/10.54216/AJBOR.080202) |
| Chicago | Marwa M. Eid, El-Sayed M. El-Kenawy, Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid, Mohamed Saber. "Bank Marketing Data Classification Using Optimized Voting Ensemble, Sine Cosine, and Genetic Algorithms." Journal of American Journal of Business and Operations Research, 8 no. 2 (2022): 16-24 (Doi : https://doi.org/10.54216/AJBOR.080202) |
| Harvard | Marwa M. Eid, El-Sayed M. El-Kenawy, Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid, Mohamed Saber. (2022). Bank Marketing Data Classification Using Optimized Voting Ensemble, Sine Cosine, and Genetic Algorithms. Journal of American Journal of Business and Operations Research, 8 ( 2 ), 16-24 (Doi : https://doi.org/10.54216/AJBOR.080202) |
| Vancouver | Marwa M. Eid, El-Sayed M. El-Kenawy, Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid, Mohamed Saber. Bank Marketing Data Classification Using Optimized Voting Ensemble, Sine Cosine, and Genetic Algorithms. Journal of American Journal of Business and Operations Research, (2022); 8 ( 2 ): 16-24 (Doi : https://doi.org/10.54216/AJBOR.080202) |
| IEEE | Marwa M. Eid, El-Sayed M. El-Kenawy, Abdelhameed Ibrahim, Abdelaziz A. Abdelhamid, Mohamed Saber, Bank Marketing Data Classification Using Optimized Voting Ensemble, Sine Cosine, and Genetic Algorithms, Journal of American Journal of Business and Operations Research, Vol. 8 , No. 2 , (2022) : 16-24 (Doi : https://doi.org/10.54216/AJBOR.080202) |