An Exploration of H-1B Visa Applications in the United States
Habeeb Hooshmand, Joseph Martinsen, Jonathan Arauco, Alishah, Dholasaniya, Bhavik Bhatt

TL;DR
This paper analyzes H-1B visa application data to identify signals predicting approval or denial, using exploratory analysis and machine learning classifiers to understand factors influencing decisions.
Contribution
It provides a data-driven exploration of H-1B visa applications and compares machine learning models for predicting application outcomes.
Findings
Certain application features are strong predictors of approval or denial
Machine learning classifiers achieve high accuracy in outcome prediction
Insights can inform future policy and application strategies
Abstract
The H-1B visa program is a very important tool for US-based businesses and educational institutes to recruit foreign talent. While the ultimate decision to certify an application lies with the United States Department of Labor, there are signals that can be used to determine whether an application is likely to be certified or denied. In this paper we first perform a data-driven exploratory analysis. We then leverage the features to train several classifiers and compare their performance. Finally, we discuss the implications of this work and future work that can be done in this area.
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Taxonomy
TopicsGlobal trade and economics · Labor market dynamics and wage inequality · Migration and Labor Dynamics
