Capstone Report

RUBEN ADAD
Nov/17/2015

Introduction

The purpose of this project is to come up with some interesting questions using data from the “Yelp Data Set Challenge”. The questions I addressed were:

  1. Which kind of reviews (positive or negative) impact more business attendance (check-in).
  2. Investigate which attributes impact more on business attendance (check-in) by category.
  3. Find the most reliable users based on their number of reviews, duration as yelp user, the number of tips, number of compliments, number of fans and number of friends.

Data preparation

  • I transformed the JSON data into a relational format because SQL is a more powerful language for exploratory analysis.
  • The following diagram shows the resulting entity relationship model.

datamodel

Methods

  1. Impact of positive/negative reviews on check-in
    • Correlation between stars and check-in
    • Tree model for predicting stars based on check-in
  2. Which attributes impact more on business attendance
    • Random forest models for predicting check-in
    • Obtain top 10 more important variables from the model
    • Create new models with the top 10 variables and compare
  3. Find most reliable users
    • Clustering to find the group of users with more activity in Yelp
    • Network analysis on “friends” network to find the users with greater betweenness and page rank

Results

  1. Impact of positive/negative reviews on check-in
    • I didn't found any relation between stars and check-in
  2. Which attributes impact (increase/decrease) business attendance
    • Restaurants: ambience divey, caters, wi-fi, etc.
    • Shopping: parking lot, price range, dogs allowed, etc.
  3. Find most reliable users
    • Those having on average 1,600 reviews, 400 fans, 1,000 friends, 15,000 compliments, etc
    • List of the top influential users according to their friends network

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