ITC516 Data Mining and Visualisation for Business Intelligence- The Data Variables

Conduct Principal Component Analysis (PCA) on the data. Evaluate and comment on the Results. Should the data be normalized Discuss what characterizes the components you consider key and justify your answer.Briefly explain advantages and any disadvantages of using the PCA compared to other methods for this task.

Answer:

ID	Age	Experience	Income	ZIP Code	Family	CCAvg	Education	Mortgage	Personal Loan	Securities Account	CD Account	Online	CreditCard
1	25	1	49	91107	4	1.60	1	0	0	1	0	0	0
2	45	19	34	90089	3	1.50	1	0	0	1	0	0	0
3	39	15	11	94720	1	1.00	1	0	0	0	0	0	0
4	35	9	100	94112	1	2.70	2	0	0	0	0	0	0
5	35	8	45	91330	4	1.00	2	0	0	0	0	0	1
6	37	13	29	92121	4	0.40	2	155	0	0	0	1	0
7	53	27	72	91711	2	1.50	2	0	0	0	0	1	0
8	50	24	22	93943	1	0.30	3	0	0	0	0	0	1
9	35	10	81	90089	3	0.60	2	104	0	0	0	1	0
10	34	9	180	93023	1	8.90	3	0	1	0	0	0	0
11	65	39	105	94710	4	2.40	3	0	0	0	0	0	0
12	29	5	45	90277	3	0.10	2	0	0	0	0	1	0
13	48	23	114	93106	2	3.80	3	0	0	1	0	0	0
14	59	32	40	94920	4	2.50	2	0	0	0	0	1	0
15	67	41	112	91741	1	2.00	1	0	0	1	0	0	0
16	60	30	22	95054	1	1.50	3	0	0	0	0	1	1
17	38	14	130	95010	4	4.70	3	134	1	0	0	0	0
18	42	18	81	94305	4	2.40	1	0	0	0	0	0	0
19	46	21	193	91604	2	8.10	3	0	1	0	0	0	0
20	55	28	21	94720	1	0.50	2	0	0	1	0	0	1
21	56	31	25	94015	4	0.90	2	111	0	0	0	1	0
22	57	27	63	90095	3	2.00	3	0	0	0	0	1	0
23	29	5	62	90277	1	1.20	1	260	0	0	0	1	0
24	44	18	43	91320	2	0.70	1	163	0	1	0	0	0
25	36	11	152	95521	2	3.90	1	159	0	0	0	0	1
26	43	19	29	94305	3	0.50	1	97	0	0	0	1	0
27	40	16	83	95064	4	0.20	3	0	0	0	0	0	0
28	46	20	158	90064	1	2.40	1	0	0	0	0	1	1
29	56	30	48	94539	1	2.20	3	0	0	0	0	1	1
30	38	13	119	94104	1	3.30	2	0	1	0	1	1	1
31	59	35	35	93106	1	1.20	3	122	0	0	0	1	0
32	40	16	29	94117	1	2.00	2	0	0	0	0	1	0
33	53	28	41	94801	2	0.60	3	193	0	0	0	0	0
34	30	6	18	91330	3	0.90	3	0	0	0	0	0	0
35	31	5	50	94035	4	1.80	3	0	0	0	0	1	0
36	48	24	81	92647	3	0.70	1	0	0	0	0	0	0
37	59	35	121	94720	1	2.90	1	0	0	0	0	0	1
38	51	25	71	95814	1	1.40	3	198	0	0	0	0	0
39	42	18	141	94114	3	5.00	3	0	1	1	1	1	0
40	38	13	80	94115	4	0.70	3	285	0	0	0	1	0
41	57	32	84	92672	3	1.60	3	0	0	1	0	0	0
42	34	9	60	94122	3	2.30	1	0	0	0	0	0	0
43	32	7	132	90019	4	1.10	2	412	1	0	0	1	0
44	39	15	45	95616	1	0.70	1	0	0	0	0	1	0
45	46	20	104	94065	1	5.70	1	0	0	0	0	1	1
46	57	31	52	94720	4	2.50	1	0	0	0	0	0	1
47	39	14	43	95014	3	0.70	2	153	0	0	0	1	0
48	37	12	194	91380	4	0.20	3	211	1	1	1	1	1
49	56	26	81	95747	2	4.50	3	0	0	0	0	0	1
50	40	16	49	92373	1	1.80	1	0	0	0	0	0	1
51	32	8	8	92093	4	0.70	2	0	0	1	0	1	0
52	61	37	131	94720	1	2.90	1	0	0	0	0	1	0
53	30	6	72	94005	1	0.10	1	207	0	0	0	0	0
54	50	26	190	90245	3	2.10	3	240	1	0	0	1	0
55	29	5	44	95819	1	0.20	3	0	0	0	0	1	0
56	41	17	139	94022	2	8.00	1	0	0	0	0	1	0
57	55	30	29	94005	3	0.10	2	0	0	1	1	1	0
58	56	31	131	95616	2	1.20	3	0	1	0	0	0	0
59	28	2	93	94065	2	0.20	1	0	0	0	0	0	0
60	31	5	188	91320	2	4.50	1	455	0	0	0	0	0
61	49	24	39	90404	3	1.70	2	0	0	1	0	1	0
62	47	21	125	93407	1	5.70	1	112	0	1	0	0	0
63	42	18	22	90089	1	1.00	1	0	0	0	0	0	0
64	42	17	32	94523	4	0.00	2	0	0	0	0	1	0
65	47	23	105	90024	2	3.30	1	0	0	0	0	0	0
66	59	35	131	91360	1	3.80	1	0	0	0	0	1	1
67	62	36	105	95670	2	2.80	1	336	0	0	0	0	0
68	53	23	45	95123	4	2.00	3	132	0	1	0	0	0
69	47	21	60	93407	3	2.10	1	0	0	0	0	1	1
70	53	29	20	90045	4	0.20	1	0	0	0	0	1	0
71	42	18	115	91335	1	3.50	1	0	0	0	0	0	1
72	53	29	69	93907	4	1.00	2	0	0	0	0	1	0
73	44	20	130	92007	1	5.00	1	0	0	0	0	0	1
74	41	16	85	94606	1	4.00	3	0	0	0	0	1	1
75	28	3	135	94611	2	3.30	1	0	0	0	0	0	1
76	31	7	135	94901	4	3.80	2	0	1	0	1	1	1
77	58	32	12	91320	3	0.30	3	0	0	0	0	0	0
78	46	20	29	92220	3	0.50	2	0	0	0	0	0	0

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