The aim is to find an optimal ML model (Decision Tree, Random Forest, Bagging or Boosting Classifiers with Hyper-parameter Tuning) to predict visa statuses for work visa applicants to US. This will help decrease the time spent processing applications (currently increasing at a rate of >9% annually) while formulating suitable profile of candidates more likely to have the visa certified.
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Delete RochitaSundar_EasyVisa_EnsembleML(Bagging_Boosting).pptx
cc6845cView on GitHubRename RochitaSundar_EasyVisa_EnsembleML(Bagging_Boosting).ipynb to Code_EnsembleML(Bagging_Boosting).ipynb
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