Restaurateur's dilemma: Solved

The rise of peer-to-peer platforms leads customers to base their opinion on electronic word of mouth more than before, which necessitates the update of factors affecting check-in behavior of customers. Though many industries have been part of this tendency, hospitality industry comes in sight most especially. In this article, the author defines the strongest influences affecting consumer decision-making process when opting for a restaurant. By processing Yelp open-source dataset and applying 9 machine learning models, it becomes clear that Random Forest model is the best tool to predict check-in behavior of diners. On top of that, the number of photos, number of friends are the best electronic factors, while trendiness, kid-friendliness and timing are the most contributing traditional aspects.