Open Access

ARTICLE

A Boosted Tree-Based Predictive Model for Business Analytics

Mohammad Al-Omari¹, Fadi Qutaishat¹, Majdi Rawashdeh¹, Samah H. Alajmani², Mehedi Masud^3,*

1 Department of Business Information Technology, Princess Sumaya University for Technology, Amman, Jordan
2 Department of Information Technology, College of Computer and Information Technology, Taif University, P.O. Box. 11099, Taif, 21994, Saudi Arabia
3 Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

* Corresponding Author: Mehedi Masud. Email:

Intelligent Automation & Soft Computing 2023, 36(1), 515-527. https://doi.org/10.32604/iasc.2023.030374

Received 24 March 2022; Accepted 15 June 2022; Issue published 29 September 2022

Abstract

Business Analytics is one of the vital processes that must be incorporated into any business. It supports decision-makers in analyzing and predicting future trends based on facts (Data-driven decisions), especially when dealing with a massive amount of business data. Decision Trees are essential for business analytics to predict business opportunities and future trends that can retain corporations’ competitive advantage and survival and improve their business value. This research proposes a tree-based predictive model for business analytics. The model is developed based on ranking business features and gradient-boosted trees. For validation purposes, the model is tested on a real-world dataset of Universal Bank to predict personal loan acceptance. It is validated based on Accuracy, Precision, Recall, and F-score. The experiment findings show that the proposed model can predict personal loan acceptance efficiently and effectively with better accuracy than the traditional tree-based models. The model can also deal with a massive amount of business data and support corporations’ decision-making process.

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Keywords

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