Baker Bank & Trust, Inc. is interested in identifying different attributes of its customers, and below is the sample data of 30 customers. For a Personal loan, 0 represents a customer who has not taken a personal loan, and 1 represents a customer who has taken a personal loan.

Use k-Nearest Neighbors (KNN) approach to classify the data, setting k-nearest neighbors with up to k = 5 (cutoff value = 0.5). Use Age and Income as input variables and Personal loan as the output variable. Be sure to normalize input data (i.e., using z-score) if necessary and classify a new client Billy Lees (33 years old, $ 80 k income) personal loan status (i.e., whether he has taken a personal loan) based on the similarity to the values of Age and Income of the observations in the training set (the 30 customer sample data).

(Hints: you may want to use Euclidean distance to assess the nearest neighbor observations)

Obs.	Age	Income (in $1000s)	Personal loan
1	47	53	1
2	26	22	1
3	38	29	1
4	37	32	1
5	44	32	0
6	55	45	0
7	44	50	0
8	30	22	0
9	63	56	0
10	34	23	0
11	52	29	1
12	55	34	1
13	52	45	1
14	63	23	1
15	51	32	0
16	41	21	1
17	37	43	1
18	46	23	1
19	30	18	1
20	48	34	0
21	50	21	1
22	56	24	0
23	35	23	1
24	39	29	1
25	48	34	0
26	51	39	1
27	27	26	1
28	57	49	1
29	33	39	1
30	58	32	0