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AWARDS

Awards

TAMU Datathon, Texas A&M University, Third Prize, 2019
Capstone Design, Gangneung-Wonju National University, First Prize, 2018
LEGO Mindstorm Robot Competition, Gangneung-Wonju National University, Grand Prize, 2016
Design Project with Arduino, Yeungnam University, Third Prize, 2016
English Speech Contest, Gangneung-Wonju National University, Second Prize, 2016
University IP-Challenge, International Intellectual Property Training Institute, Third Prize, 2013
Korea Student Invention Exhibition, Korean Invention Promotion Association, Third Prize, 2007

TAMU Datathon Competition

Title: Recommendation system for a group of users

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

Hosted by Major League Hackathon and Facebook