Eatsy is a regression model that models what preferences are important to individual users and extrapolates this data to recommend a restaurant that satisfies the preferences for a group of users. It takes into account reviews on other online platforms and user-specific preferences and past experiences to find the perfect restaurant for everyone.
- Models with all the data in Yelp's open dataset that holds information on 200,000 businesses, over 6 million reviews, and 1.6 million users
- Implements machine learning techniques to predict the importance of certain preferences to a user
- Combines the importance of preferences of every user in a group optimally
- Uses a logistic regression model to calculate a score for each restaurant given a group of users
- Selects the top five restaurants that produce the highest scores for the group of users
- Install dependencies with
pip install -r requirements.txt - Start the server with
python main.py - The server should now be running at
localhost:5000
eatsy was created by Michael Sprintson (michaelsprintson), Timothy Goh (tGoh98), Sanghyeon Lee (SangHyeonLee), and Yong Shin (yowashi23) for TAMU Datathon 2019. Read more about it in the Devpost.