Honors College Capstone Experience/Thesis Projects

Department

Economics

Additional Departmental Affiliation

Mathematics

Document Type

Thesis

Abstract

The purpose of this research is to look at the relationship that market-specific, economic, and demographic variables have with the success of farmers markets in Kentucky. It additionally seeks to build a tool for predicting farmers market success that could be used by policy makers to aid in decision-making processes concerning farmers markets. Logistic regression and Support Vector Machines (SVMs) are used on data acquired from the Kentucky Department of Agriculture and the American Community Survey in order to analyze the data in a traditional statistical approach as well as a machine learning approach. The results included an SVM model that had an accuracy of 83.3% in predicting farmers market success. Additionally, both methods produced models that found population size, number of vendors, and number of years the market has been established as important predictors for farmers market success.

Advisor(s) or Committee Chair

David Zimmer, Ph.D.

Disciplines

Applied Statistics | Economics | Other Computer Sciences

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