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🍻Uses Google, Yelp, and Foursquare APIs to retrieve and rank bars
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LICENSE
README.md
api_keys.py
business.py
data.csv
foursquare.py
google.py
main.py
yelp.py

README.md

Guesstimator

Guesstimator is a program that searches the Google Places, Yelp, and Foursquare APIs to create a list of bars in the given location. Then, it combines their ratings as a Bayesian estimate to rank them more accurately.

Setup

You'll need to acquire API keys for each of the individual services and add them to api_keys.py.

You'll also need to install rauth for authentication.

pip install rauth

Usage

Search Radius (meters): 40000
Lat: 41.5908
Long: -93.6208
Would you like more points? (y/n) n
Searching foursquare at lat: 41.5908 long: -93.6208 ...
Searching google at lat: 41.5908 long: -93.6208 ...
Searching yelp at lat: 41.5908 long: -93.6208 ...
Found 90 total businesses!

Other Usages

This program could also be used to search restaurants, coffee shops, you name it. You'll need to:

  • Update the Foursquare CATEGORY_ID
  • Change the type parameter in the Google Search URL
  • Modify the term parameter in the Yelp Search URL

Review the API documentation for each service to determine what types are allowed.

Output

  1. El Bait Shop - 8.93 with 3 sources
  2. Hessen Haus - 8.43 with 3 sources
  3. Zombie Burger + Drink Lab - 8.73 with 2 sources
  4. The Royal Mile - 8.28 with 3 sources
  5. Confluence Brewing Company - 9.15 with 1 source
  6. High Life Lounge - 8.53 with 2 sources
  7. Louie’s Wine Dive - 8.45 with 2 sources
  8. Up-Down - 8.42 with 2 sources
  9. Exile Brewing Company - 8.88 with 1 source
  10. Court Avenue Restaurant & Brewing Company - 8.26 with 2 sources

If you look at the raw data.csv, you'll notice I removed results that were primarily restaurants and adjusted the rankings based on the number of data sources. This isn't a perfect rating system, but it's an improvement from using a single source.

Issues

You'll have to create an edge case if two bars somehow happen to have the same address. You should never have more data sources in your .csv file than search engine modules. If you do, then two bars with the same address are getting bucketed together.

More Infomation

The Google Places API returns the most "prominent" businesses first, unless specified. Prominence can be affected by a place's ranking in Google's index, global popularity, and other factors. If I switch that to location first, I do receive more unique places, but of less quality.

The same theory applies to the Yelp Search API. You can choose the sort value, either by best matched, highest rated, or distance. The default gives you the best data, but has the potential to leave out businesses if they don't exceed the threshold.

This is why some places might not be ranked as high as they (potentially) should be. When using the above strategies, I was able to find more businesses. However, there was a decrease in the quality of ratings, and it brought the overall averages down. I'm now left with a completely different set of data. This might be better suited for your needs, or maybe not. Here's how you could accomplish that:

Yelp - Add these parameters to your request

params['sort'] = 1
params['limit'] = 20

Google - Remove the radius parameter and add this to your URL

&rankby=distance
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