Abstract
The study of consumer payment choice at the point of sale involves a classification of payment methods such as cash, credit cards, debit cards, prepaid cards, paper checks, and electronic payments withdrawn from consumers' bank account. I describe alternative methods for studying consumer payment choice using some machine learning techniques applied to consumer diary survey data. The results are then compared to the more traditional logistic regression methods. Machine learning techniques have advantages in generating predictions of payment choice, in visualization of the results, and when applied to high-dimensional data. The logistic regression approach has an advantage in interpreting the probability that a buyer uses a specific payment instrument.
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