| Summary: | 碩士 === 國立臺灣大學 === 經濟學研究所 === 106 === In the recent years, numerous commerces have gradually shifted from physi- cal store to web-shops, so-called the e-commerce. These online stores contain lots of log files in the back-end which basically record the pages accessed by visitors, namely the clickstream data. In this study, we predict consumers’ purchase decision by analyzing the clickstream data from an online wine re- tailer. We impose two modern machine learning model, decision tree and ran- dom forest, to predict consumers’ final purchase intention. Besides the normal features based on visitors’ activities on the website, we construct a new feature that clusters different groups of visitors according to the sequence page-type accessed. After re-sampling to remedy the unbalanced data, our two models both show high predictive accuracy up to 90% and provides a new insight for retailer to target some specific visitors on website.
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