Abstract
The research on machine leaning method for bank marketing has become a widely discussed topic. To obtain precise performances for bank marketing, KNN algorithm is utilized in this paper to forecast the success rate of telemarketing that focuses on whether a prospect will agree to a term deposit if a phone call is placed. The comparison experiments with three other machine learning models (Decision Tree, Random Forrest, and Naive Bayes) by using real data from a Portuguese banking institution revealed that KNN achieved the highest Accuracy (89.45 percent) and Precision (61.76 percent), thus proving the effectiveness of KNN. The proposed prediction method can also be adapted to operate within many other situations, creating a template when faced with the issue of direct marketing such as the increasing of the efficiency and profitability of telemarketing.
Reference12 articles.
1. Pradap R, Kamaludeen P. Machine learning models for bank telemarketing classification and prediction. The International Journal of Analytical and Experimental Modal Analysis, 2019, 6(8): 962-967.
2. Moro S, Cortez P, Rita P. A data-driven approach to predict the success of bank telemarketing. Elsevier, 2014, 62: 22-31.
3. Moro S, Cortez P, Rita P. Using data mining for bank direct marketing: an application of the CRISP-DM methodology. Proceedings of the European Simulation and Modelling Conference, 2011.
4. Guliyev H, Tatoğlu F. Customer churn analysis in banking sector: evidence from explainable machine learning models. Journal of Applied Microeconometrics, 2021, 1(2): 85-99.
5. King R S. Cluster analysis and data Mining: an introduction. Mercury Learning and Information, 2015.