M.S. Applied Data Science - Capstone Chronicles 2025

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strengthened both the analytical rigor and the clarity of the final article. I also want to thank my classmates for their ongoing collaboration, especially those who shared insights on preprocessing decisions, feature engineering strategies, and the interpretation of disability-related ACS variables. Their comments helped refine several methodological choices and ensured stronger alignment across project components. Finally, I would like to acknowledge the U.S. Census Bureau for providing high-quality ACS and PUMS datasets, without which this analysis would not have been possible. Their continued commitment to accessible public data makes work like this achievable. https://doi.org/10.1111/gean.12273 Kim, W., Torres, E., & Zhao, H. (2022). Educational attainment as a determinant of socioeconomic disadvantage. Social Indicators Research, 162(1), 71 – 89. https://doi.org/10.1007/s11205-021-02846-9 O’Neil, C., Graves, E., & Sullivan, L. (2021). Intersecting inequalities: Disability, race, and rural disadvantage in the U.S. Journal of Social Issues, 77(2), 415 – 437. https://doi.org/10.1111/josi.12412 She, L., & Carter, M. (2023). Disability, labor-force barriers, and poverty in the United States. Journal of Social Policy, 52(3),455 – 472. https://doi.org/10.1017/S004727942200005X U.S. Census Bureau. (2024). American Community Survey(ACS)methodologyoverview. https://www.census.gov/programs surveys/acs/methodology.html Johnson, R., & Patel, S. (2023). Local labor-market structures and disability income disparities in post-industrial regions. Journal of Economic Inequality Research, 18(2), 145 – 162 . https://doi.org/10.1177/08912424231123456 References (APA 7) Chen, J., & Rhee, S. (2020). Neighborhood-level variation in poverty and economic mobility. Geographical Analysis, 52(4), 623 – 642.

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