An analysis of non-immigrant work visas in the USA using Machine Learning

High-skilled immigrants are a very important factor in US innovation and entrepreneurship, accounting for roughly a quarter of US workers in fields such as computer science and delivering in terms of patents or firm starts. Their contributions to the US is rapidly increasing in the past three decades and are found to be well trained and skilled on average than their native counterparts. While the impact of these high-skilled workers is signified, the way in which they compete to enter a tech hub like the US is rather not fair. H-1B, the work visa to import high-skilled workers, is not used for high skilled anymore but rather used to import cheap labor to displace native workers in many cases. Many billionaires, experts, pundits and even the government are looking for many amendments in H-1B to abolish this by bringing in a merit system or increasing the minimum wages to awarding these visas. We attempt to analyze the petitions filed from 2011-16 and classify the petitions filed as positive or negative, indicating whether the petition is highly skilled or not. After classifying, we build a model using Random Forest to predict any visa petition in any state of US as positive or negative. Experimental results show the companies that are classified as abusing these visas (negative) are well consistent with the ones shown in reports and news articles.