Major
Economics
Access Type
Open Access
Abstract
The purpose of this study was to determine the best place to open a food truck in Chicago by creating a predictive model formed from nationwide demographic data. The model was trained on data from over 200 US ZIP codes, to identify patterns that might predict the viability of a given area’s food truck market. Our Random Forest classification model identified bike commuters, millennials, density, and more as predictors of market success, and consistently predicted the Loop as the most viable area for food trucks in Chicago. This suggests that food truck entrepreneurs in Chicago should focus on these markets.
Community Partners
Blow Wow Jamaican Cuisine
Faculty Mentors & Instructors
Prof. Carolyn Kmet
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Using ZIP-Level Population Data to Predict Food Truck Success in Chicago
The purpose of this study was to determine the best place to open a food truck in Chicago by creating a predictive model formed from nationwide demographic data. The model was trained on data from over 200 US ZIP codes, to identify patterns that might predict the viability of a given area’s food truck market. Our Random Forest classification model identified bike commuters, millennials, density, and more as predictors of market success, and consistently predicted the Loop as the most viable area for food trucks in Chicago. This suggests that food truck entrepreneurs in Chicago should focus on these markets.