American Journal of Business and Operations Research AJBOR 2692-2967 2770-0216 10.54216/AJBOR https://www.americaspg.com/journals/show/2312 2018 2018 Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA Ahmed Mohamed Zaki Department of Civil and Architectural Engineering, University of Miami, Coral Gables, FL, USA Nima Khodadadi Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia Wei Hong Lim Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia S. K. Towfek Direct marketing strategies in the banking sector have undergone evolution with the integration of predictive analytics and machine learning techniques. The focus of this study is on the utilization of these technologies to foresee bank term deposit subscriptions. The methodology encompasses data exploration, visualization, and the implementation of machine learning models. Datasets from Kaggle are employed, relationships within the data are explored through crosstabulations and heat maps, and feature engineering and preprocessing techniques are applied. The study individually implements models such as SGD Classifier, k-nearest neighbor Classifier, and Random Forest Classifier. The results indicate that the best performance among the evaluated models was exhibited by the Random Forest Classifier, achieving an accuracy of 87.5%, a negative predictive value (NPV) of 92.9972%, and a positive predictive value (PPV) of 87.8307%. These findings provide valuable insights for banks seeking to optimize their marketing strategies within the dynamic landscape of the financial industry. 2024 2024 79 88 10.54216/AJBOR.110110 https://www.americaspg.com/articleinfo/1/show/2312