3 Project Proposal

Project Description

• The goal of our project is to use data science in order to uncover patterns in the data that we collect to find if restaurants in affluent areas get better reviews and ratings. The term “affluent” for our description will be based on census data using household median income in order to find places in which these areas could play a factor in restaurant prices and ratings. The area we will be focusing on is in and around the Seattle area as we know that there is a mix of socio-economic zones. Based on our previous knowledge, we have assumed that more affluent areas tend to have more higher-end restaurants, and area’s that are not as wealthy have will have more restaurants with a more affordable price range. However, even with us knowing about the price of of these restaurants, our research will mainly focus on the differences between the reviews and ratings of restaurants in different communities. The data we will be using comes from the US census data through the seattle government website. The data will have information on all census tracts in which we will extract city borders and calculate median household incomes. Using this income data, we can create a clear distinction between affluent and non-affluent areas. To obtain the restaurant data, we will be using the Yelp Api found on their website. With this API, we can extract information on Seattle restaurants in different areas and also extract their ratings and reviews so that we can try to make a connection between the restaurant location, its current reviews and ratings and it’s surrounding economic status.
• Restaurant-goers and foodies are who we believe are our key stakeholders, and there are several reasons. First of all, comparing with other people, they care more about the food quality and popularity. Online platform, such as Yelp, has become the most popular app where they could proactively search for these information, besides asking friends around for recommendations. However, with the problem assumption that restaurants in affluent areas get better reviews and ratings, they might not get the most correct information about restaurants ratings. Our data science project can help them access to better-evaluated data on restaurants ratings by extracting information on restaurants and see if there’s correlation between its reviews, locations and socio-economic status.
• The main goal of our project is to support our audience make better decisions when they use any business-review. Specifically, users can understand that with our project, they’ll be able to distinguish there’s a biased difference between restaurants in different areas, if our assumption is proved correct. So for foodies and restaurants goers who care about restaurant qualities based on online platform, they’ll be more carefully making their decisions after using those platforms, such as Yelp, Google reviews, etc.

1. Location of affluent and non-affluent areas (by median household income):
2. Extract rating, price, reviews form the online platform we’re using.
3. Analyze and make connections.

Technical Description

Logistics