Check out my Medium post " Discover Your Next Favorite Restaurant — Exploration and Visualization on Yelp Dataset" here.
Check out my Kaggle kernel here.
I explored the business data within the Yelp dataset, and examined the restaurant ratings among some of the fast food restaurant chains. We then took a look at different restaurant attributes and their relationships. At last, there was an example of how we can find top restaurants that fit our needs and used the tip data to create visualizations that can help us better understand the restaurant tips.
As of March 2020, there are 211 million cumulative reviews on Yelp. With this massive amount of data, Yelp also releases a subset of their businesses, reviews, and user data for educational and academic purposes. There is a lot of information that can be mined in this dataset and can be used to infer meaning, business attributes, and sentiment.
- Peek at the Business Data
- Geographic Visualizations
- Rating Comparisons Amond Popular Restaurant Chains
- Examine Relationships between Attributes
- Discover Restaurants According to Our Needs and Create Visualizations
I did my analysis through Kaggle kernel and I recommended you to do so as well, mostly based on two reasons:
- The size of Yelp dataset is quite large but it is pre-loaded through Kaggle kernel so you don't need to download it locally.
- Most libraries are already available in this environment so no need to install more libraries locally.