  In this paper, we use machine learning techniques to explore the H-1B
application dataset disclosed by the Department of Labor (DOL), from 2008 to
2018, in order to provide more stylized facts of the international workers in
US labor market. We train a LASSO Regression model to analyze the impact of
different features on the applicant's wage, and a Logistic Regression with
L1-Penalty as a classifier to study the feature's impact on the likelihood of
the case being certified. Our analysis shows that working in the healthcare
industry, working in California, higher job level contribute to higher
salaries. In the meantime, lower job level, working in the education services
industry and nationality of Philippines are negatively correlated with the
salaries. In terms of application status, a Ph.D. degree, working in retail or
finance, majoring in computer science will give the applicants a better chance
of being certified. Applicants with no or an associate degree, working in the
education services industry, or majoring in education are more likely to be
rejected.
