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Analysing Different Machine Learning Algorithms to Study Visa Decisions in the United States

Kulkarni, Devika Chandrashekhar (2022) Analysing Different Machine Learning Algorithms to Study Visa Decisions in the United States. Masters thesis, Dublin, National College of Ireland.

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For those wishing to relocate, the United States is the most favored choice. A variety of visas are offered depending on the sort of profession. Obtaining a visa in the United States is difficult in most situations. The reason for this is because many people are unaware of the factors that determine whether or not a visa is granted. To figure this out, you’ll need a powerful model that can forecast an individual’s situation. Despite the fact that there are several established efforts on visa prediction in the United States. However, they all concentrate on the H1b visa, which is the most frequent and in-demand visa category. This study is concerned with the prediction of a wide range of visa kinds, not simply H1b. It will also provide the knowledge about the various sorts of visas available, the opportunities and organizations that can sponsor them as well as state wise distribution of the visa status. This project analyses the different kind of visas and to do this, five models were run and compared (Multilinear Logistic Regression, Random Forest Classifier, AdaBoost with LR, Radius Neighbor Classifier and XGBoost) to find the best fit model. It was found that among all the models, Random Forest gave the best results with around 93% accuracy. Other models also gave satisfactory results. The results of each model is discussed in chapter 4.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Prediction; Employment; Visa; Data mining algorithms; Random Forest
Subjects: J Political Science > JV Colonies and colonization. Emigration and immigration. International migration
Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 22 Feb 2023 13:50
Last Modified: 02 Mar 2023 09:38

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