Ideas for Future Projects

  • Partnership with a company like Yelp that collects this data so it is applicable to more restaurants
  • Pipeline/Scheduled SQS of AWS lambda scraping to EC2 instance so we are automatically updating restaurant information
  • Functionality for users to rate restaurants they indicate they are attending and use this data to retrain the machine learning model
  • Use REACT Native to create a mobile-friendly app for the Apple App Store and/or Google Play Store
  • During the restaurant recommendation process, ask the user more specific questions to get more specific recommendations. Example questions include parking information, travel information, and more details about allergies/restrictions.

What to be Aware of

  • The restaurant database does not have every available restaurant.
  • This is only applicable to DC (20037 restaurants)
  • If someone is very allergic, this can greatly limit the number of possible places they go. We will not recommend something that does not satisfy their allergy needs, so they could get no results because their allergies cannot be accommodated
  • We will not recommend a restaurant that is not open at the time of the search. If there are no nearby restaurants that satisfy their requirements and are open, there will be no results.

Ideas for Next Steps

  • Partner with reservation company (like OpenTable or Resy), so after users get a recommendation, they can make a reservation for that restaurant at their selected time.
  • Distribute a LetsEat Newsletter to promote new restaurants and features. This can also allow for ads as a revenue method.
  • Partner with restaurants to have a featured restaurants section. This can act as a revenue method.