A Deep Learning-Based Approach for Predicting the Outcome of H-1B Visa Application
- 3 Downloads
The H-1B is a visa that allows US employers to employ foreign workers in specialty occupations. The number of H-1B visa applicants is growing drastically. Due to a heavy increment in the number of applications, the lottery system has been introduced, since only a certain number of visas can be issued every year. But, before a Labor Condition Application (LCA) enters the lottery pool, it has to be approved by the US Department of Labor (DOL). The approval or denial of this visa application depends on a number of factors such as salary, work location, full-time employment, etc. The purpose of this research is to predict the outcome of an applicant’s H-1B visa application using artificial neural networks and to compare the results with other machine learning approaches.
KeywordsH-1B Labor condition application Deep learning Artificial neural networks
- 2.Dreher, Axel, and Panu Poutvaara. 2005. Student flows and migration: An empirical analysis.Google Scholar
- 3.Jing-Lin. H-1B Visa Data Analysis and Prediction by using K-means Clustering and Decision Tree Algorithms. [Online] Available: https://github.com/Jinglin-LI/H1B-Visa-Prediction-by-Machine-Learning-Algorithm/blob/master/H1B%20Prediction%20Research%20Report.pdf.
- 4.H-1B Visa Petitions 2011–2016—Kaggle. [Online]. Available: https://www.kaggle.com/nsharan/h-1b-visa/data. Accessed October 20, 2017.
- 5.Seo, Songwon. 2006. A review and comparison of methods for detecting outliers in univariate data sets. Master’s Thesis, University of Pittsburgh (Unpublished).Google Scholar
- 6.Glorot, Xavier, Antoine Bordes and Y. Bengio. 2010. Deep sparse rectifier neural networks. Journal of Machine Learning Research 15.Google Scholar
- 7.Kingma, Diederik P., Ba Adam Jimmy. A method for stochastic optimization.Google Scholar