Task $1$:

Import the raw data $($CC$\_$Default.csv$)$ into your Jupyter notebook.

$1\mathrm{.}1$ Check if the data is loaded correctly by printing a few observations. Check the total number of observations and variables.

$1\mathrm{.}2$ Provide the descriptive statistics and manipulate data.

a$.$ Check for missing values if any.

b$.$ Plot the univariate distribution $($Atleast $10$ plots$)$

c$.$ Convert the relevant variables such as payment variables $($Pay$0-$Pay$6$ and customer related variables$)$ to categorical variables as appropriate.

$1\mathrm{.}3$ Find the variables that are correlated and the variables that might help in finding the defaulters next month using a few plots. The plots should provide insights on the following:

a$.$ The independent variable that should help identify those who will default from the next month$$s credit card payment

b$.$ The relation between dependent and independent variables

c$.$ The correlations among the variables, etc.

$1\mathrm{.}4$ Provide your insights into the variables and their relationship based on your analysis in Task $1\mathrm{.}3$ 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.payment.next.month Shows customers who defaulted on their payments on the following month: $1=$ yes $0=$ no