Skip to content

Classified the restaurants into two tiers based on their user ratings. Used Random Forest to predict the Important Attributes which contribute to the success rate of a Restaurant. Used logistic regression to predict which tier has better success rate based on certain attributes using SAS.

Notifications You must be signed in to change notification settings

mginamdar/Classification-of-YELP.com-Restaurant-Ratings

Repository files navigation

Classification-of-YELP.com-Restaurant-Ratings

Yelp provides academic students access to their data to use it in an innovative way and break ground in research. In this project, we target on the business reviews and star rating for restaurants only. We are trying to identify the key attributes or features that the consumer is looking for their best dining experience. We are using three different algorithms such as logistic regression, random forest and principal component analysis to create our models. After analyzing the performance of each models, the best model for predicting the ratings from reviews and star rating is the random forest algorithm, which exhibited an accuracy of 82%, which is better than the other algorithms that we used in this project.

Please go through the Project Report pdf which shared in this repo.

About

Classified the restaurants into two tiers based on their user ratings. Used Random Forest to predict the Important Attributes which contribute to the success rate of a Restaurant. Used logistic regression to predict which tier has better success rate based on certain attributes using SAS.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages