Advanced Visa Outcome Predictions for Superior Accuracy and Interpretability

Nallasivam, Kishore (2024) Advanced Visa Outcome Predictions for Superior Accuracy and Interpretability. Masters thesis, Dublin, National College of Ireland.

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Abstract

The H1B visa plays an important role for skilled workers looking for employment in the U.S.; however, its application process is unclear and inconsistent, presenting challenges for employers. This study addresses these issues using two robust predictive models: the Bi-LSTM model for sequential data and XGBoost for structured data analysis to predict H1B visa outcomes with high accuracy and interpretability. This research applies advanced feature selection and data balancing methods to H1B visa data from the 2017 to 2022 fiscal years to address class imbalances and achieve highly generalized models. The deep learning and machine learning models are employed to find a factor influencing visa decisions. More complex sequential dependencies are generated with the help of Bi-LSTM, while enhanced scalability and interpretability are derived from XGBoost. As evaluation measures, accuracy, F1 score, and recall were adopted. These metrics show improved forecasting and efficiency, along with greater transparency in the decision-making process. It offers practical recommendations for applicants and immigration authorities while offering a starting point for applying predictive modeling to additional concept classes. To eliminate unfair practices in the issuance of visas, the study aims at making the process more transparent and efficient.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Clifford, William UNSPECIFIED
Uncontrolled Keywords:	Deep learning; H1B visa; Employment; visa outcome prediction; XGBoost
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management > Human Resource Management > Equal Opportunity in Employment J Political Science > JV Colonies and colonization. Emigration and immigration. International migration > International Migration > Immigration Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	03 Sep 2025 14:57
Last Modified:	03 Sep 2025 14:57
URI:	https://norma.ncirl.ie/id/eprint/8757

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