age	income	gender	Residential cluster	There are other loan	married	Loan status
22	7500	MALE	1	NO	YES	AGREED
22	6500	MALE	2	NO	YES	AGREED
53	5000	MALE	3	NO	YES	rejected
30	7800	MALE	1	YES	YES	AGREED
21	7700	FEMALE	1	NO	NO	AGREED
43	6700	FEMALE	2	NO	NO	AGREED
21	4900	MALE	3	NO	NO	AGREED
60	3500	MALE	4	NO	YES	rejected
22	6800	MALE	2	NO	YES	AGREED
28	2500	MALE	4	NO	NO	AGREED
42	5500	MALE	2	YES	YES	AGREED
22	2300	FEMALE	4	NO	YES	rejected
23	8300	FEMALE	1	YES	NO	AGREED
63	7400	MALE	3	NO	YES	rejected
28	7900	MALE	1	YES	NO	AGREED
48	8400	MALE	4	NO	YES	rejected
19	7500	FEMALE	1	YES	NO	AGREED
32	5100	MALE	2	NO	NO	AGREED
27	6000	MALE	2	YES	NO	AGREED

1. From the data above, make a decision tree using one of the data mining tools / manual calculations? (if you want to make a range / interval it can be assumed)
2. From the decision tree results. Make a rule that determines which customer is approved for borrowing!
3. As in this table, whether the loan status is approved or rejected ?

age	income	gender	Residential cluster	There are other loan	married	Loan status
29	80	male	3	yes	no	?