Abstract
The dataset for the ASA 2017 Datafest competition was provided by Expedia Inc., a travel company that primarily runs travel fare aggregator websites. The dataset includes over 10 million user records of searches and purchases through various Expedia websites. This paper conducts a machine learning analysis via a classification decision tree to identify potential customers who do not purchase a travel package but are similar to those who do. The paper then narrows down on the countries a group of potential customers is most likely to travel to as well as the types of hotels. The paper investigates the group’s preferences by looking at factors such as hotels’ price range, star rating, and brand. These features create a predictive model that help suggest to Expedia the niche markets to focus on to convert non-consumers to those who purchase travelling packages and as a corollary to increase revenue.