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Garsa3112
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AssignmentDataset

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train · 200 rows

CustomerID int64 1 200	Gender stringclasses 2 values	Age int64 18 70	Annual Income (k$) int64 15 137	Spending Score (1-100) int64 1 99
1	Male	19	15	39
2	Male	21	15	81
3	Female	20	16	6
4	Female	23	16	77
5	Female	31	17	40
6	Female	22	17	76
7	Female	35	18	6
8	Female	23	18	94
9	Male	64	19	3
10	Female	30	19	72
11	Male	67	19	14
12	Female	35	19	99
13	Female	58	20	15
14	Female	24	20	77
15	Male	37	20	13
16	Male	22	20	79
17	Female	35	21	35
18	Male	20	21	66
19	Male	52	23	29
20	Female	35	23	98
21	Male	35	24	35
22	Male	25	24	73
23	Female	46	25	5
24	Male	31	25	73
25	Female	54	28	14
26	Male	29	28	82
27	Female	45	28	32
28	Male	35	28	61
29	Female	40	29	31
30	Female	23	29	87
31	Male	60	30	4
32	Female	21	30	73
33	Male	53	33	4
34	Male	18	33	92
35	Female	49	33	14
36	Female	21	33	81
37	Female	42	34	17
38	Female	30	34	73
39	Female	36	37	26
40	Female	20	37	75
41	Female	65	38	35
42	Male	24	38	92
43	Male	48	39	36
44	Female	31	39	61
45	Female	49	39	28
46	Female	24	39	65
47	Female	50	40	55
48	Female	27	40	47
49	Female	29	40	42
50	Female	31	40	42
51	Female	49	42	52
52	Male	33	42	60
53	Female	31	43	54
54	Male	59	43	60
55	Female	50	43	45
56	Male	47	43	41
57	Female	51	44	50
58	Male	69	44	46
59	Female	27	46	51
60	Male	53	46	46
61	Male	70	46	56
62	Male	19	46	55
63	Female	67	47	52
64	Female	54	47	59
65	Male	63	48	51
66	Male	18	48	59
67	Female	43	48	50
68	Female	68	48	48
69	Male	19	48	59
70	Female	32	48	47
71	Male	70	49	55
72	Female	47	49	42
73	Female	60	50	49
74	Female	60	50	56
75	Male	59	54	47
76	Male	26	54	54
77	Female	45	54	53
78	Male	40	54	48
79	Female	23	54	52
80	Female	49	54	42
81	Male	57	54	51
82	Male	38	54	55
83	Male	67	54	41
84	Female	46	54	44
85	Female	21	54	57
86	Male	48	54	46
87	Female	55	57	58
88	Female	22	57	55
89	Female	34	58	60
90	Female	50	58	46
91	Female	68	59	55
92	Male	18	59	41
93	Male	48	60	49
94	Female	40	60	40
95	Female	32	60	42
96	Male	24	60	52
97	Female	47	60	47
98	Female	27	60	50
99	Male	48	61	42
100	Male	20	61	49

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