Supervised ML Algorithms with Hyperparameter Tuning
If it were only this simple…
The Immigration and Nationality Act (INA) of the US permits foreign workers to come to the United States to work on either a temporary or permanent basis. The act also protects US workers against adverse impacts on their wages or working conditions by ensuring US employers' compliance with statutory requirements when they hire foreign workers to fill workforce shortages. The immigration programs are administered by the Office of Foreign Labor Certification (OFLC).
In FY 2016, the OFLC processed 775,979 employer applications for 1,699,957 positions for temporary and permanent labor certifications. This was a nine percent increase in the overall number of processed applications from the previous year. The process of reviewing every case is becoming a tedious task as the number of applicants is increasing every year.
Pushing a candidate through the approval process costs both time and money. The model gives an edge to hiring managers by allowing them to focus on candidates more likely to be approved given their background, work history, job location in the US, desired salary etc.
EDA
Built Bagging, Random Forest, Adaboost, Gradient Boost, XGBoost and Stacking models
Hyperparameter tuning with GridsearchCV to optimize model performance
