Please click on one of the markers in the map above to get more information... Scroll to zoom (data for several cities are available).
An Attempted SolutionWe construct a "model" of how various attributes affect a restaurant's average Yelp rating. This is done with a random forest regressor. The model learns which combinations of attributes tend to yield a good rating. Then for each restaurant, given its attributes, we check what change can be made to maximize the model's predicted rating. The top 10 recommended changes are shown in the second table (below).
(please click on a marker)
One must carefully interpret these top 10 recommendations. The individual restaurants may or may not have control over these attributes (e.g. outdoor seating, parking), but they do provide some insight into how valuable these features are, so they are not omitted from the table. Also, the model reflects some correlations which should not necessarily be taken as "recommendations". For example, restaurants with a higher price range tend to do better (maybe they are in nicer neighborhoods or are fancier in some way) but this does not mean that raising prices would automatically give a higher Yelp rating (probably the opposite would be true).
Some restaurants do not have any attribute information (other than the location). For these the top 10 recommended attribute changes tell us what the model "thinks" is particularly important for that type of restaurant. For Starbucks, McDonald's, Subway, and Taco Bell, respectively, RestaurantsPriceRange=$$, RestaurantsPriceRange=$$$, BikeParking=True, and RestaurantsGoodForGroups=True are the most important attributes.
Modified March 11th, 2020. Martin Carrington, Fellow at The Data Incubator.