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Predicting Restaurant Success via Neighborhood Indicators

Brian de Luna, Alexander Goldberg, Gabriela Merz, Daniel Smith

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Links

  • the iPython notebook found in this github is too big for git to display, so you can read through our code here
  • The website we built to display our results (check out the integration with Google Maps API) can be found here

Motivation and Background

Where will a restaurant be most successful? Will a Chinese restaurant do better in Chinatown, or in an area where there aren't any Chinese places? This project sought to figure out what neighborhood factors impact restaurant success. Using various statistical models (Ridge Regressions, Random Forest, the ensemble method, and more) we were able to predict a restaurant's yelp! star rating with a root mean squared error of .67.

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for more findings, please read through our iPython notebook and check out our website

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our analysis of the yelp dataset for the cs109 final project

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