UCLA

This thesis presents a study on location optimization for franchisee restaurants using Graph Neural Network (GNN) models, namely GCN, GAT, and GraphSAGE. The research employs these models to analyze geographic coordinates and other relevant data to predict optimal franchise locations. By incorporating real-world data such as Yelp reviews, census information, and city demographics, the study attempts to model the significant factors that influence the success of franchise locations. The primary contribution of this work is the development of a tool that aids market research teams in making informed decisions about where to establish new restaurant outlets and optimizing location selection through advanceddata analytics and machine learning techniques. Among the three models, the GraphSAGE model performed best. It achieved a loss of 0.48, an accuracy of 77%, and an ROC score of 0.78, outperforming the other models across various assessments.