Problem Statement
Context:
Business communities in the United States are facing high demand for human resources, but one of the constant challenges is identifying and attracting the right talent, which is perhaps the most important element in remaining competitive. Companies in the United States look for hard-working, talented, and qualified individuals both locally as well as abroad.
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).
OFLC processes job certification applications for employers seeking to bring foreign workers into the United States and grants certifications in those cases where employers can demonstrate that there are not sufficient US workers available to perform the work at wages that meet or exceed the wage paid for the occupation in the area of intended employment.
Objective:
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.
The increasing number of applicants every year calls for a Machine Learning based solution that can help in shortlisting the candidates having higher chances of VISA approval. OFLC has hired the firm you work for to deliver a data-driven solutions. You as a data analyst have to analyze the data provided and provide actionable insights, with the help of EDA, recommend a suitable profile for the applicants for whom the visa should be certified or denied based on the drivers that significantly influence the case status.
Data Description
The data contains the different attributes of employee and the employer. The detailed data dictionary is given below.
case_id: ID of each visa application
continent: Information of continent the employee
education_of_employee: Information of education of the employee
has_job_experience: Does the employee has any job experience? Y= Yes; N = No
requires_job_training: Does the employee require any job training? Y = Yes; N = No
no_of_employees: Number of employees in the employer's company
yr_of_estab: Year in which the employer's company was established
region_of_employment: Information of foreign worker's intended region of employment in the US.
prevailing_wage: Average wage paid to similarly employed workers in a specific occupation in the area of intended employment. The purpose of the prevailing wage is to ensure that the foreign worker is not underpaid compared to other workers offering the same or similar service in the same area of employment.
unit_of_wage: Unit of prevailing wage. Values include Hourly, Weekly, Monthly, and Yearly.
full_time_position: Is the position of work full-time? Y = Full Time Position; N = Part Time Position
case_status: Flag indicating if the Visa was certified or denied
The objective of this analysis is to find patterns within the dataset to gain further understanding of the data and leverage it to choose a machine learning algorithm that can recommend a suitable profile for the applicants whose visa should be certified or denied