Predicting U.S. metro status using structural cost-of-living shares and machine learning.
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Updated
Dec 8, 2025 - Python
Predicting U.S. metro status using structural cost-of-living shares and machine learning.
This project analyzes how SNAP participation rates relate to structural cost-of-living patterns across U.S. counties. Using PCA, regression, and clustering on county-level cost shares, income, and demographics, the study identifies key affordability gradients and regional disparities that shape SNAP usage.
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