As a Data Scientist write a python script for the below project with an extensive Exploratory Data analysis:

Need help with the python script for the below problem statement using Machine Learning models as a data scientist at EasyVisa have to analyze the data provided and provide solutions:

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 EasyVisa for data$-$driven solutions. You as a data scientist at EasyVisa have to analyze the data provided and, with the help of a classification model:

Facilitate the process of visa approvals.

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 the 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

Kindly make sure that all the required information asked in the rubric is included and do an extensive EDA with as many possibilities.

Include a detailed explanation of the approach taken, inferences, and insights

Include outputs such as graphs, tables, and all other relevant information

Criteria

Exploratory Data Analysis

$-$ Problem definition $-$ Univariate analysis $-$ Bivariate analysis $-$ Use appropriate visualizations to identify the patterns and insights $-$ Key meaningful observations on individual variables and the relationship between variables

Data Preprocessing

$-$ Prepare the data for analysis $-$ Feature Engineering $-$ Missing value Treatment $-$ Outlier Treatment $-$ Ensure no data leakage among train$-$test and validation sets

Model Building $-$ Original Data

$-$ Choose the appropriate metric for model evaluation $-$ Build $5$ models $($from decision trees, bagging and boosting methods$)-$ Comment on the model performance $*$ You can choose NOT to build XGBoost if you are facing issues with the installation

Model Building $-$ Oversampled Data

$-$ Oversample the train data $-$ Build $5$ models $($from decision trees, bagging and boosting methods$)-$ Comment on the model performance $*$ You can choose NOT to build XGBoost if you are facing issues with the installation