For this dataset, you are required to fit a decision tree classifier.

 

Credit Score	Income	Employment Status	Years at Current Address	Loan Amount	Loan Approval
720	$60,000	Employed	5	$10,000	Approved
650	$40,000	Unemployed	2	$5,000	Denied
800	$90,000	Employed	10	$20,000	Approved
600	$30,000	Self-Employed	3	$8,000	Denied
700	$80,000	Employed	7	$15,000	Approved
750	$70,000	Employed	8	$12,000	Approved
670	$45,000	Unemployed	1	$4,000	Denied
680	$55,000	Self-Employed	4	$7,000	Denied
730	$65,000	Employed	6	$9,000	Approved
620	$35,000	Self-Employed	2	$6,000	Denied

i) There are 5 independent variables, some of which are continuous variables, how to you split their continuous variables in nodes? Use the first column "Credit Score" as an example to show your algorithm and calculation. 

ii) for the first splitting node, which column should be split first? Use calculation to show why