2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 11 , Issue 1 , PP: 79-88, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Mohamed Zaki 1 * , Nima Khodadadi 2 , Wei Hong Lim 3 , S. K. Towfek 4
Doi: https://doi.org/10.54216/AJBOR.110110
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.
Direct Marketing , Predictive Analytics , Machine Learning , Bank Term Deposit Subscriptions , Data Exploration , Feature Engineering.
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