Loan Purpose	Checking	Savings	Months Customer	Months Employed	Credit Risk
Furniture	638	347	13	14	High
Education	963	4754	40	45	Low
Small Appliance	966	0	25	4	High
Furniture	0	485	37	23	High
Business	16647	895	16	34	Low
Other	218	0	49	0	Low
New Car	162	595	22	10	Low
Used Car	0	789	25	28	Low
Furniture	0	717	37	60	High
Furniture	0	800	13	69	High
Education	0	3281	19	20	High
Education	1257	0	10	65	Low
Furniture	305	492	19	1	Low
New Car	9621	308	25	41	High
Used Car	0	178	13	89	High
Furniture	0	479	19	0	High
Small Appliance	0	4449	25	87	High
Small Appliance	0	325	19	13	High
New Car	942	3036	25	36	Low
Small Appliance	0	643	19	6	Low
Large Appliance	0	775	19	8	High
New Car	8176	12230	7	5	Low
Furniture	0	461	13	48	Low
New Car	644	1571	19	1	High
New Car	0	605	37	20	High
Furniture	352	7525	13	4	Low
New Car	305	4553	7	2	High
Business	663	0	19	57	Low
Used Car	1336	0	37	11	Low
Furniture	0	180	5	2	Low
Education	8122	136	22	4	High
Furniture	0	734	37	111	High
New Car	19812	0	25	37	High
Furniture	0	169	19	6	High
Used Car	219	841	43	0	Low
Furniture	757	208	25	36	High
New Car	0	857	11	34	Low
Furniture	0	5857	19	20	Low
New Car	0	713	13	29	High
New Car	0	648	15	57	High
New Car	0	716	19	33	High
Furniture	759	596	10	18	High
Small Appliance	0	302	10	30	High
Small Appliance	0	425	13	10	High
Furniture	0	204	31	5	High
Furniture	0	3369	25	17	Low
New Car	122	460	37	109	High
New Car	0	761	25	92	High
Used Car	0	0	11	4	Low
New Car	0	712	16	6	High

Use the given data to classify the record below using the kNN algorithm for k=1 to 5 . Complete the table below for the nearest neighbors and for the classification of the new record for each value of k. Use the given data to classify the record below using the kNN algorithm for k=1 to 5 . Complete the table below for the nearest neighbors and for the classification of the new record for each value of k