IN PYTHON ONLY

For this case, you need to apply the decision rules and cutoff probability of $0\mathrm{.}5$ from Section

to classify the two loans in Table $10$ of the article$$Should This Loan be Approved or Denied?$$: A Large Dataset with Class Assignment Guidelines

as $$higher risk$$ or $$lower risk$$ for loan approval by writing Python code to reproduce results $($not format$)$ in Tables $7($a$),8,9$ of this article using the SBA case data SBAcase$\mathrm{.}11\mathrm{.}13\mathrm{.}17.$csv$.$ The variable $$Selected$$ indicates which observations are the $$training$$ data and which are the $$testing$$ data $(1=$ training data to be used to build the model, $0=$ testing data to validate the model$).$ Partition the data using this variable.

Ignore parts a b$,$ c and d$.$

$($e$)$ For the California$-$based example, the final model with the risk indicators in Table $8$ is used to estimate the probability of default for the two loan applications. Use Python to predict the probability of default for Carmichael Realty $($Loan $1)$ and SV Consulting $($Loan $2).$ Applying the decision rules and cutoff probability of $0\mathrm{.}5$ from Section $4\mathrm{.}3$

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$,$ how should these two loans be classified as$,$ lower risk $($approve$)$ or higher risk $($deny$)?$

$($f$)$ The default cutoff value of $0\mathrm{.}5$ is used in conjunction with the probability of default. Compute the threshold that should be used if we want to make a classification based on the odds of default, and the threshold for the corresponding logit$.$

$($g$)$ When a $$higher risk$$more likely to default$$ loan is misclassified as $$lower risk$$more likely to pay in full$$ loan, the misclassification cost is much higher than when a $$lower risk$$more likely to pay in full$$ loan is misclassified as $$higher risk$$more likely to default$.$ To minimize the expected cost of misclassification, should the cutoff value for classification $($which is currently at $0\mathrm{.}5)$ be increased or decreased?

$($h$)$ Review Python documentation: sklearn.linear$\_$model.LogisticRegression

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$$and example code from the class.$$Fit a logistic regression model to reproduce results $($not format$)$ in Tables $7($a$),8,9$ of this article using the SBA case data SBAcase$\mathrm{.}11\mathrm{.}13\mathrm{.}17.$csv by using $(1)$ sklearn LogisticRegression$()$ liblinear solver and $(2)$ sklearn LogisticRegression$()$ Default Solver $\text{'}$lbfgs$\text{'}.$ Adjust parameters such as "penalty" and "tol" as needed.