Yelp business rating classification using hybrid ensemble

An algorithm that can predict the review rating of a potential business with only existing information about the location and business categories would be an invaluable tool in making investment decisions. Utilizing the Yelp business dataset, we built a model, that can do as such, by classifying whether a potential business belongs to a positively-reviewed class (star ratings greater than or equal to 4) or a negatively-reviewed class (star ratings less than 4) given its location in latitude and longitude and the categories the potential business belongs to. More specifically, we applied a feature engineering technique using Extremely Randomized Trees, and constructed a hybrid ensemble classifier using neural network, decision tree, and logistic regression. We compared our model with other popular ensemble algorithms such as random forest and neural network ensemble, and our hybrid ensemble model generates the best result with an accuracy rate of 67.37% and AUC of 0.7322.