Data
User.ID Gender Age EstimatedSalary Purchased
1 15624510 Male 19 19000 0
2 15810944 Male 35 20000 0
3 15668575 Female 26 43000 0
4 15603246 Female 27 57000 0
5 15804002 Male 19 76000 0
6 15728773 Male 27 58000 0
7 15598044 Female 27 84000 0
8 15694829 Female 32 150000 1
9 15600575 Male 25 33000 0
10 15727311 Female 35 65000 0
11 15570769 Female 26 80000 0
12 15606274 Female 26 52000 0
13 15746139 Male 20 86000 0
14 15704987 Male 32 18000 0
15 15628972 Male 18 82000 0
16 15697686 Male 29 80000 0
17 15733883 Male 47 25000 1
18 15617482 Male 45 26000 1
19 15704583 Male 46 28000 1
20 15621083 Female 48 29000 1
21 15649487 Male 45 22000 1
22 15736760 Female 47 49000 1
23 15714658 Male 48 41000 1
24 15599081 Female 45 22000 1
25 15705113 Male 46 23000 1
26 15631159 Male 47 20000 1
27 15792818 Male 49 28000 1
28 15633531 Female 47 30000 1
29 15744529 Male 29 43000 0
30 15669656 Male 31 18000 0
31 15581198 Male 31 74000 0
32 15729054 Female 27 137000 1
33 15573452 Female 21 16000 0
34 15776733 Female 28 44000 0
35 15724858 Male 27 90000 0
36 15713144 Male 35 27000 0
37 15690188 Female 33 28000 0
38 15689425 Male 30 49000 0
39 15671766 Female 26 72000 0
40 15782806 Female 27 31000 0
41 15764419 Female 27 17000 0
42 15591915 Female 33 51000 0
43 15772798 Male 35 108000 0
44 15792008 Male 30 15000 0
45 15715541 Female 28 84000 0
46 15639277 Male 23 20000 0
47 15798850 Male 25 79000 0
48 15776348 Female 27 54000 0
49 15727696 Male 30 135000 1
50 15793813 Female 31 89000 0
