Analysing Socio-Economic Patterns Across U.S. States Using Clustering Techniques

In this project, I explore the complex socio-economic landscape across various states in the United States, focusing on the interplay between employment, education, healthcare occupations, industry sectors, and median household income. Utilizing hierarchical and K-means clustering techniques to analyze 2021 U.S. Census data, I dissect five pivotal variables to uncover the underlying economic vitality and educational landscape shaping different regions.

The analysis reveals distinctive patterns and variations among the states. Clusters formed around states with strong healthcare and educational sectors highlight regional economic hubs dominated by these industries. Other clusters, grouped by similarities in median household incomes and employment sectors, point to shared economic conditions and opportunities. Notably, states such as California and New York emerge as outliers due to their substantial populations and diverse economic landscapes.

This project sheds light on the socio-economic stratification across the United States, providing a comprehensive view of how employment status, educational attainment, and industry focus collectively influence regional economic realities. The insights gained are invaluable for policymakers, economists, and researchers in crafting targeted economic development strategies that address regional disparities and optimize local strengths. By applying meticulous data analysis and advanced clustering methods, I offer a detailed portrayal of the American economic landscape, aiming to inform decision-making and strategic planning in socio-economic policy and practice.