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Dynamic parameters and algorithm in predicting bank telemarketing success

Author

Listed:
  • Theera Prompreing
  • Kattareeya Prompreing
  • Genesis Sembiring Depari
  • Jen-peng Huang

Abstract

In order to keep competing in a competitive market, it is important to have an accurate prediction of which customers are most likely to buy products or services. Data mining method is one of the useful techniques that experts use to deal with this problem. However, there are a bunch of algorithms that can be employed. The question of which algorithm should be used is still a hot issue today. This research aims to find the best machine learning algorithm in predicting telemarketing success, especially for targeting potential customers. We examined eight machine learning algorithms such, deep neural network (deep learning), naive Bayes, generalised linear model, logistic regression, decision tree, random forest, support vector machine, and gradient boosted tree along with adaptive parameters to each of the algorithms. The results show that, gradient boosted trees outperform the other seven algorithms which achieve 91.3% accuracy.

Suggested Citation

  • Theera Prompreing & Kattareeya Prompreing & Genesis Sembiring Depari & Jen-peng Huang, 2022. "Dynamic parameters and algorithm in predicting bank telemarketing success," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 40(3), pages 399-414.
    • Handle: RePEc:ids:ijbisy:v:40:y:2022:i:3:p:399-414
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