Abstract
Competent marketers aim to accurately predict consumer desires and needs. With the advancement of machine learning, different machine learning models could be applied to solve various challenges, including precisely determining consumer behavior. Meanwhile, discount coupons are a frequent marketing approach to boost sales and encourage recurring business from existing customers. Accordingly, the current study seeks to analyze customer behavior by assessing an in-vehicle coupon recommendation system dataset as the case study. The dataset, which was obtained from the University of California-Irvine (UCI) machine learning repository, could predict consumer decisions as to whether accept the discount coupon influenced by demographic and environmental factors, such as driving destinations, age, current time, and weather. This study also compared six machine learning classification models, including Bayesian Network (BayesNet), Naïve Bayes, Instance-Bases Learning with Parameter-K (Lazy-IBK), Tree J48, Random Forest, and RandomTree, on the dataset to identify a suitable model for predicting customer behavior through two test modes, namely cross-validation and percentage split. The model performance was evaluated by analyzing factors, such as accuracy, precision, processing time, recall, and F-measure, for the model development. The findings discovered that Naïve Bayes and Lazy-IBK consumed the least amount of prediction time, although with the lowest accuracy. RandomTree was the highest processing time, whereas Random Forest provided the highest accuracy, precision, recall and F-measure values.
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Acknowledgment
The authors would like to thank the Universiti Teknologi MARA for funding the research project under MyRa Grant Scheme File No.: 600-RMC/GPM LPHD 5/3 (180/2021). The authors gratefully acknowledge the College of Computing, Informatics and Media, Universiti Teknologi MARA, Cawangan Negeri Sembilan for supporting the publication of this paper.
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Rusli, N.I.A., Zulkifle, F.A., Ramli, I.S. (2023). A Comparative Study of Machine Learning Classification Models on Customer Behavior Data. In: Yusoff, M., Hai, T., Kassim, M., Mohamed, A., Kita, E. (eds) Soft Computing in Data Science. SCDS 2023. Communications in Computer and Information Science, vol 1771. Springer, Singapore. https://doi.org/10.1007/978-981-99-0405-1_16
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