$(*$COMPLETE ALL PARTS IN PYTHON CODE$*)$ Dream Housing Finance company deals in all home loans. They have a presence across all urban, semi$-$urban, and rural areas.

The company wants to automate the loan eligibility process $($real$-$time$)$ based on customer details provided while filling the online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History, and others. A description of the variables in the data set is as follows:

$\backslash$table$[[$Variable Name,Description$],[$Gender$,$Male$/$ Female$],[$Married$,$Applicant married $($Y$/$N$)],[$Dependents$,$Number of dependents$],[$Education$,$Applicant Education $($Graduate$/$ Under Graduate$)],[$Self$\_$Employed,Self$-$employed $($Y$/$N$)],[$ApplicantIncome$,$Applicant income $($in $ thousands$)],[$LoanAmount$,$Loan amount $($in $ thousands$)],[$Loan$\_$Amount$\_$Term,Term of the loan $($in months$)],[$Credit$\_$History,credit history meets guidelines Yes$)],[$Property$\_$Area,Urban$/$Semi$-$Urban$/$ Rural$],[$Loan$\_$Status,Loan approved $($Y$/$N$)]]$

a$.$ Create a classification tree for predicting Loan$\_$Status. Use cost$-$complexity pruning to determine the optimal tree. Include all steps $($including any useful plots$)$ in the decision process and be sure to display your final tree.

b$.$ What is the test error associated with your final tree?