Task $2$:

Import $$Train$.$csv$$ into your Jupyter notebook.

$2\mathrm{.}1$ Check the total number of observations and print a few records. Please note that the variable conversion in the raw data. Hint: Convert the relevant variables such as payment variables, Pay$0-$Pay$6,$ and customer related variables $($demographic$)$ to categorical variables as appropriate.

$2\mathrm{.}2$ Fit a logistic regression after making the dataset balanced. Hint: Use class weight parameter.

$2\mathrm{.}3$ Remove the variable$($s$)$ that would cause multicollinearity. Explicitly state the variable$($s$)$ that you are dropping in a markdown cell in your Jupyter notebook. Hint: To remove a variable, use the drop function.

Import $$Test$.$csv$$ into your Jupyter notebook.

$2\mathrm{.}4$ Test the model on the test dataset. Please note that the variable conversion in the raw data. Hint: Convert the relevant variables such as payment variables, Pay$0-$Pay$6,$ and customer related variables $($demographic$)$ to categorical variables as appropriate.

$2\mathrm{.}5$ Plot the confusion matrix.

$2\mathrm{.}6$ Provide your insights on accuracy, precision and F$1$ Score in a markdown cell in your Jupyter notebook.

ID A numerical value assigned to each credit card customer

LIMIT$\_$BAL The remaining credit a customer can use

SEX $1=$ male ; $2=$ female

EDUCATION $1=$ graduate school ; $2=$ university ; $3=$ high school ; $4=$ others ; $5=$ unknown ; $6=$ unknown

MARRIAGE $0=$ unknown $1=$ married $2=$ single $3=$ others

AGE A customer$$s age in years

PAY$\_0$ Repayment status in September $2005$: $0$ or less: Paid duly ; $1$ or greater $=$ payment was delayed

PAY$\_2$ Repayment status in August $20050$ or less: Paid duly ; $1$ or greater $=$ payment was delayed

PAY$\_3$ Repayment status in July $20050$ or less: Paid duly ; $1$ or greater $=$ payment was delayed

PAY$\_4$ Repayment status in June $20050$ or less: Paid duly ; $1$ or greater $=$ payment was delayed

PAY$\_5$ Repayment status in May $20050$ or less: Paid duly ; $1$ or greater $=$ payment was delayed

PAY$\_6$ Repayment status in April $20050$ or less: Paid duly ; $1$ or greater $=$ payment was delayed

BILL$\_$AMT$1$ The amount in the bill statement for September $2005$ in NT dollar

BILL$\_$AMT$2$ The amount in the bill statement for August $2005$ in NT dollar

BILL$\_$AMT$3$ The amount in the bill statement for July $2005$ in NT dollar

BILL$\_$AMT$4$ The amount in the bill statement for June $2005$ in NT dollar

BILL$\_$AMT$5$ The amount in the bill statement for May $2005$ in NT dollar

BILL$\_$AMT$6$ The amount in the bill statement for April $2005$ in NT dollar

PAY$\_$AMT$1$ The amount paid in NT dollar in September $2005$

PAY$\_$AMT$2$ The amount paid in NT dollar in August $2005$

PAY$\_$AMT$3$ The amount paid in NT dollar in July $2005$

PAY$\_$AMT$4$ The amount paid in NT dollar in June $2005$

PAY$\_$AMT$5$ The amount paid in NT dollar in May $2005$

PAY$\_$AMT$6$ The amount paid in NT dollar in April $2005$

Default Shows customers who defaulted on their payments on the following month: $1=$ yes $0=$ no