Chinese consumer research in the luxury sector is the emphasis in the business research field. However, it can be cost-intensive or time-consuming to interpret big data from any research conducted in the field. In this paper, the researchers created a machine-learning model to help minimize those research barriers. This study analyzed Chinese luxury consumption behavior, while the Chinese contributed 33% of the global luxury market in 2018 and play as a growth engine in the luxury market (Bain & Company. 2019. https://www.bain.com/insights/whats-powering-chinas-market-for-luxury-goods/). The researchers interpreted this analysis using machine-learning algorithms through different sets of conditions and then proposed an understandable and highly accurate machine-learning model. Unlike traditional statistical methods, which rely on domain experts to create hand-crafted features, this paper proposes an unsupervised end-to-end model that can directly and accurately process questionnaire data without human intervention. This paper also demonstrates how to practically apply an automatic unsupervised analysis method (PCA) to find inferences in the big data, and helps interpret the implied meaning to the questions.
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