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Are all Yelp restaurant reviews created equal? Should we place greater trust in reviews made by people who know the cuisine well? How about reviews of ethnic restaurants by people of that ethnicity or reviews by seasoned Yelpers? We may not be able to find the perfect restaurant all the time, but leave it to data science to give us tools to make our choices a little easier.
Welcome to our tutorial on Yelp Review Modifications, based on a Springboard blog post by Robert Chen. The post was created as part of Robert's project in Springboard's Foundations of Data Science workshop with the help of his mentor, Andi Bandyopadhyay. Here you will learn the techniques that Robert used to tailor the Yelp star reviews for restaurants.
The following exercises on importing data, data manipulation and data visualization will teach you how to “cut through the noise” and deliver useful metrics for finding a great place to eat on Friday night! The course is an interesting take on a dilemma that we all face on a regular basis. Is the information that is available to us useful? In many cases, yes, but too often a cloud shrouds the real value that the information has. The best part of this course is getting to explore a real world problem and learning how to remove the haze for yourself!
Importing data and star review modificationFree
Weighting star reviews based on users who review more restaurants
Generating authentic star reviewsFree
Modifying restaurant reviews to only the native Indian users
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I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.
Devon Edwards Joseph
Lloyds Banking Group
DataCamp is the top resource I recommend for learning data science.
Harvard Business School
DataCamp is by far my favorite website to learn from.
Decision Science Analytics, USAA