TY  - EJOU
AU  - Al-Omari, Mohammad 
AU  - Qutaishat, Fadi 
AU  - Rawashdeh, Majdi 
AU  - Alajmani, Samah H. 
AU  - Masud, Mehedi 

TI  - A Boosted Tree-Based Predictive Model for Business Analytics
T2  - Intelligent Automation \& Soft Computing

PY  - 2023
VL  - 36
IS  - 1
SN  - 2326-005X

AB  - 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.
KW  - Business analytics; decision trees; machine learning; business value; decision making

DO  - 10.32604/iasc.2023.030374