Predicting Vehicle Driver Preference from the Analysis of In-Vehicle Coupon Recommendation Data

Murtada Dohan; Rasha Bashar Mohammed; Wisam Hazim Gwad; Mohammed Khalaf; Karam M.Z. Othman

Authors

Murtada Dohan Data Centre Unit, Division of Technology Incubator, Al-Mustaqbal University, University, 51001 Hillah, Babylon , Iraq
Rasha Bashar Mohammed Technical Engineering College for Computer and AI, Northern Technical University, Mosul, 41000, Nineveh, Iraq. https://orcid.org/0009-0005-9404-0226
Wisam Hazim Gwad Department of artificial intelligence engineering. College of engineering. Alnoor University, Ninavah 41012 ,Iraq
Mohammed Khalaf Department of Computer Sciences, College of Science, University of Al Maarif, Al Anbar,31001, Iraq
Karam M.Z. Othman Technical Engineering College for Computer and AI, Northern Technical University, Mosul, 41000, Nineveh, Iraq

Keywords:

Coupon recommendation, machine learning, classification, ensemble, k-nearest neighbor (KNN), support vector machine (SVM), and decision tree (DT).

Abstract

A coupon is a ticket or document used in marketing that may be redeemed for a monetary discount or refund when purchasing a product. The problem, in this case, is to know if a customer will accept a coupon for a particular venue. The answers that the user will drive there ‘right away’ or ‘later before the coupon expires’ are labeled as ‘Y = 1’, and the answers ‘no, I do not want the coupon’ is labeled as ‘Y = 0’. This paper proposes integrating three machine learning techniques to create an ensemble boosting classification (EBC) model for a vehicle coupon recommendation. The algorithms used are k-nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT). The results show that the EBC model surpasses the three machine learning models and achieves the highest performance of accuracy 97.37%, precision 94.14%, recall 96.41%, and F1-score 95.28%.