An Exploration of H-1B Visa Applications in the United States

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.