H1-B VISA status prediction using decision trees
In this report, we focus on analyzing the prevalence of CASE_STATUS of the H-1B visa applicants. In particular, we aim to understand the statistical distribution of the success or failure rate of the visa application, and predict the outcome of application based on information provided by a future applicant. Thus, this report proposes the use of Label Encoding method for data preprocessing/representation and random forest regression model for predicting the CASE_STATUS of visa application.
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