ml-code-generator / datasets /customer_segmentation.csv
Surbhi
Feature extraction and model training
cedd211
age,income,spending_score,segment
6,15,0.95,C
33,28,0.62,B
84,73,0.5,B
74,77,0.62,C
97,51,0.28,C
8,97,0.99,C
85,15,0.66,C
25,59,0.12,B
26,77,0.33,B
60,94,0.14,C
88,89,0.9,B
35,53,0.8,C
72,58,0.4,B
25,35,0.13,C
18,35,0.39,A
72,35,0.47,C
19,27,0.43,A
47,99,0.79,B
13,70,0.51,A
28,55,0.95,A
20,57,0.47,A
57,40,0.45,B
20,87,0.65,C
80,6,0.53,C
65,30,0.39,B
30,13,0.94,C
29,4,0.48,B
17,1,0.29,B
98,16,0.92,A
19,38,0.41,B
39,67,0.16,A
92,87,0.89,C
56,88,0.11,A
70,6,0.86,C
56,11,0.5,A
97,36,0.27,A
45,6,0.98,A
90,96,0.53,A
43,99,0.18,B
9,52,0.14,A
76,2,0.86,A
45,98,0.84,B
90,15,0.09,B
15,17,0.06,C
46,54,0.69,B
9,63,0.69,A
68,24,0.78,A
83,2,0.94,B
97,43,0.02,C
5,91,0.22,B
93,38,0.14,C
30,8,0.13,B
53,39,0.13,C
60,30,0.44,C
97,76,0.51,C
33,91,0.69,A
36,25,0.15,A
54,10,0.84,C
60,81,0.07,B
40,12,0.78,A
35,79,0.59,C
22,46,0.54,C
57,51,0.84,A
4,54,0.81,B
63,62,0.91,B
84,41,0.13,B
50,23,0.8,A
6,86,0.19,A
42,20,0.51,C
70,96,0.18,B
75,17,1.0,A
27,81,0.61,B
73,69,0.14,C
29,55,0.01,B
55,69,0.9,B
35,68,0.28,C
32,10,0.99,A
38,17,0.05,B
80,10,0.04,C
55,72,0.64,B
82,71,0.1,A
29,20,0.76,B
64,62,0.18,A
35,11,0.82,C
30,88,0.85,C
51,94,0.1,C
40,28,0.71,C
50,4,0.53,C
52,98,0.12,C
70,41,0.93,B
65,37,0.99,C
49,67,0.92,C
87,24,0.21,B
47,53,0.16,B
3,98,0.53,C
42,29,0.04,C
17,32,0.6,A
70,32,0.46,B
2,32,0.18,C
68,22,0.5,A