Feature Selection of Post-Graduation Income of College Students in the United States

TL;DR

This study identifies key factors influencing the six-year post-graduation income of US college students who received financial aid, using data analysis and machine learning to inform social stratification insights.

Contribution

It applies multiple attribute selection methods and machine learning algorithms to identify the most important predictors of post-graduation income.

Findings

Neighborhood professional degree attainment is a significant predictor.

Parental income and family college education influence income outcomes.

SAT scores are relevant but less impactful than other factors.

Abstract

This study investigated the most important attributes of the 6-year post-graduation income of college graduates who used financial aid during their time at college in the United States. The latest data released by the United States Department of Education was used. Specifically, 1,429 cohorts of graduates from three years (2001, 2003, and 2005) were included in the data analysis. Three attribute selection methods, including filter methods, forward selection, and Genetic Algorithm, were applied to the attribute selection from 30 relevant attributes. Five groups of machine learning algorithms were applied to the dataset for classification using the best selected attribute subsets. Based on our findings, we discuss the role of neighborhood professional degree attainment, parental income, SAT scores, and family college education in post-graduation incomes and the implications for social stratification.