introduction-to-machine-learning/Partie 4 - Clustering/Mall_Customers.csv

1	CustomerID	Genre	Age	Annual Income (k$)	Spending Score (1-100)
2	0001	Male	19	15	39
3	0002	Male	21	15	81
4	0003	Female	20	16	6
5	0004	Female	23	16	77
6	0005	Female	31	17	40
7	0006	Female	22	17	76
8	0007	Female	35	18	6
9	0008	Female	23	18	94
10	0009	Male	64	19	3
11	0010	Female	30	19	72
12	0011	Male	67	19	14
13	0012	Female	35	19	99
14	0013	Female	58	20	15
15	0014	Female	24	20	77
16	0015	Male	37	20	13
17	0016	Male	22	20	79
18	0017	Female	35	21	35
19	0018	Male	20	21	66
20	0019	Male	52	23	29
21	0020	Female	35	23	98
22	0021	Male	35	24	35
23	0022	Male	25	24	73
24	0023	Female	46	25	5
25	0024	Male	31	25	73
26	0025	Female	54	28	14
27	0026	Male	29	28	82
28	0027	Female	45	28	32
29	0028	Male	35	28	61
30	0029	Female	40	29	31
31	0030	Female	23	29	87
32	0031	Male	60	30	4
33	0032	Female	21	30	73
34	0033	Male	53	33	4
35	0034	Male	18	33	92
36	0035	Female	49	33	14
37	0036	Female	21	33	81
38	0037	Female	42	34	17
39	0038	Female	30	34	73
40	0039	Female	36	37	26
41	0040	Female	20	37	75
42	0041	Female	65	38	35
43	0042	Male	24	38	92
44	0043	Male	48	39	36
45	0044	Female	31	39	61
46	0045	Female	49	39	28
47	0046	Female	24	39	65
48	0047	Female	50	40	55
49	0048	Female	27	40	47
50	0049	Female	29	40	42
51	0050	Female	31	40	42
52	0051	Female	49	42	52
53	0052	Male	33	42	60
54	0053	Female	31	43	54
55	0054	Male	59	43	60
56	0055	Female	50	43	45
57	0056	Male	47	43	41
58	0057	Female	51	44	50
59	0058	Male	69	44	46
60	0059	Female	27	46	51
61	0060	Male	53	46	46
62	0061	Male	70	46	56
63	0062	Male	19	46	55
64	0063	Female	67	47	52
65	0064	Female	54	47	59
66	0065	Male	63	48	51
67	0066	Male	18	48	59
68	0067	Female	43	48	50
69	0068	Female	68	48	48
70	0069	Male	19	48	59
71	0070	Female	32	48	47
72	0071	Male	70	49	55
73	0072	Female	47	49	42
74	0073	Female	60	50	49
75	0074	Female	60	50	56
76	0075	Male	59	54	47
77	0076	Male	26	54	54
78	0077	Female	45	54	53
79	0078	Male	40	54	48
80	0079	Female	23	54	52
81	0080	Female	49	54	42
82	0081	Male	57	54	51
83	0082	Male	38	54	55
84	0083	Male	67	54	41
85	0084	Female	46	54	44
86	0085	Female	21	54	57
87	0086	Male	48	54	46
88	0087	Female	55	57	58
89	0088	Female	22	57	55
90	0089	Female	34	58	60
91	0090	Female	50	58	46
92	0091	Female	68	59	55
93	0092	Male	18	59	41
94	0093	Male	48	60	49
95	0094	Female	40	60	40
96	0095	Female	32	60	42
97	0096	Male	24	60	52
98	0097	Female	47	60	47
99	0098	Female	27	60	50
100	0099	Male	48	61	42
101	0100	Male	20	61	49
102	0101	Female	23	62	41
103	0102	Female	49	62	48
104	0103	Male	67	62	59
105	0104	Male	26	62	55
106	0105	Male	49	62	56
107	0106	Female	21	62	42
108	0107	Female	66	63	50
109	0108	Male	54	63	46
110	0109	Male	68	63	43
111	0110	Male	66	63	48
112	0111	Male	65	63	52
113	0112	Female	19	63	54
114	0113	Female	38	64	42
115	0114	Male	19	64	46
116	0115	Female	18	65	48
117	0116	Female	19	65	50
118	0117	Female	63	65	43
119	0118	Female	49	65	59
120	0119	Female	51	67	43
121	0120	Female	50	67	57
122	0121	Male	27	67	56
123	0122	Female	38	67	40
124	0123	Female	40	69	58
125	0124	Male	39	69	91
126	0125	Female	23	70	29
127	0126	Female	31	70	77
128	0127	Male	43	71	35
129	0128	Male	40	71	95
130	0129	Male	59	71	11
131	0130	Male	38	71	75
132	0131	Male	47	71	9
133	0132	Male	39	71	75
134	0133	Female	25	72	34
135	0134	Female	31	72	71
136	0135	Male	20	73	5
137	0136	Female	29	73	88
138	0137	Female	44	73	7
139	0138	Male	32	73	73
140	0139	Male	19	74	10
141	0140	Female	35	74	72
142	0141	Female	57	75	5
143	0142	Male	32	75	93
144	0143	Female	28	76	40
145	0144	Female	32	76	87
146	0145	Male	25	77	12
147	0146	Male	28	77	97
148	0147	Male	48	77	36
149	0148	Female	32	77	74
150	0149	Female	34	78	22
151	0150	Male	34	78	90
152	0151	Male	43	78	17
153	0152	Male	39	78	88
154	0153	Female	44	78	20
155	0154	Female	38	78	76
156	0155	Female	47	78	16
157	0156	Female	27	78	89
158	0157	Male	37	78	1
159	0158	Female	30	78	78
160	0159	Male	34	78	1
161	0160	Female	30	78	73
162	0161	Female	56	79	35
163	0162	Female	29	79	83
164	0163	Male	19	81	5
165	0164	Female	31	81	93
166	0165	Male	50	85	26
167	0166	Female	36	85	75
168	0167	Male	42	86	20
169	0168	Female	33	86	95
170	0169	Female	36	87	27
171	0170	Male	32	87	63
172	0171	Male	40	87	13
173	0172	Male	28	87	75
174	0173	Male	36	87	10
175	0174	Male	36	87	92
176	0175	Female	52	88	13
177	0176	Female	30	88	86
178	0177	Male	58	88	15
179	0178	Male	27	88	69
180	0179	Male	59	93	14
181	0180	Male	35	93	90
182	0181	Female	37	97	32
183	0182	Female	32	97	86
184	0183	Male	46	98	15
185	0184	Female	29	98	88
186	0185	Female	41	99	39
187	0186	Male	30	99	97
188	0187	Female	54	101	24
189	0188	Male	28	101	68
190	0189	Female	41	103	17
191	0190	Female	36	103	85
192	0191	Female	34	103	23
193	0192	Female	32	103	69
194	0193	Male	33	113	8
195	0194	Female	38	113	91
196	0195	Female	47	120	16
197	0196	Female	35	120	79
198	0197	Female	45	126	28
199	0198	Male	32	126	74
200	0199	Male	32	137	18
201	0200	Male	30	137	83