Medical_insurance_dataset.xlsx

	A	B	C	D	E	F
1	User ID	Gender	Age	AgeGroup	EstimatedSalary	Purchased

2	15624510	Male	19	18-20	19000	not-purchased
3	15810944	Male	35	31-40	20000	not-purchased
4	15668575	Female	26	21-30	43000	not-purchased
5	15603246	Female	27	21-30	57000	not-purchased
6	15804002	Male	19	18-20	76000	not-purchased
7	15728773	Male	27	21-30	58000	not-purchased
8	15598044	Female	27	21-30	84000	not-purchased
9	15694829	Female	32	31-40	150000	purchased
10	15600575	Male	25	21-30	33000	not-purchased
11	15727311	Female	35	31-40	65000	not-purchased
12	15570769	Female	26	21-30	80000	not-purchased
13	15606274	Female	26	21-30	52000	not-purchased
14	15746139	Male	20	18-20	86000	not-purchased
15	15704987	Male	32	31-40	18000	not-purchased
16	15628972	Male	18	18-20	82000	not-purchased
17	15697686	Male	29	21-30	80000	not-purchased
18	15733883	Male	47	41-50	25000	purchased
19	15617482	Male	45	41-50	26000	purchased
20	15704583	Male	46	41-50	28000	purchased
21	15621083	Female	48	41-50	29000	purchased
22	15649487	Male	45	41-50	22000	purchased
23	15736760	Female	47	41-50	49000	purchased
24	15714658	Male	48	41-50	41000	purchased
25	15599081	Female	45	41-50	22000	purchased
26	15705113	Male	46	41-50	23000	purchased
27	15631159	Male	47	41-50	20000	purchased
28	15792818	Male	49	41-50	28000	purchased
29	15633531	Female	47	41-50	30000	purchased
30	15744529	Male	29	21-30	43000	not-purchased
31	15669656	Male	31	31-40	18000	not-purchased
32	15581198	Male	31	31-40	74000	not-purchased
33	15729054	Female	27	21-30	137000	purchased
34	15573452	Female	21	21-30	16000	not-purchased
35	15776733	Female	28	21-30	44000	not-purchased
36	15724858	Male	27	21-30	90000	not-purchased
37	15713144	Male	35	31-40	27000	not-purchased
38	15690188	Female	33	31-40	28000	not-purchased
39	15689425	Male	30	21-30	49000	not-purchased
40	15671766	Female	26	21-30	72000	not-purchased
41	15782806	Female	27	21-30	31000	not-purchased
42	15764419	Female	27	21-30	17000	not-purchased
43	15591915	Female	33	31-40	51000	not-purchased
44	15772798	Male	35	31-40	108000	not-purchased
45	15792008	Male	30	21-30	15000	not-purchased
46	15715541	Female	28	21-30	84000	not-purchased
47	15639277	Male	23	21-30	20000	not-purchased
48	15798850	Male	25	21-30	79000	not-purchased
49	15776348	Female	27	21-30	54000	not-purchased
50	15727696	Male	30	21-30	135000	purchased
51	15793813	Female	31	31-40	89000	not-purchased
52	15694395	Female	24	21-30	32000	not-purchased
53	15764195	Female	18	18-20	44000	not-purchased
54	15744919	Female	29	21-30	83000	not-purchased
55	15671655	Female	35	31-40	23000	not-purchased
56	15654901	Female	27	21-30	58000	not-purchased
57	15649136	Female	24	21-30	55000	not-purchased
58	15775562	Female	23	21-30	48000	not-purchased
59	15807481	Male	28	21-30	79000	not-purchased
60	15642885	Male	22	21-30	18000	not-purchased
61	15789109	Female	32	31-40	117000	not-purchased
62	15814004	Male	27	21-30	20000	not-purchased
63	15673619	Male	25	21-30	87000	not-purchased
64	15595135	Female	23	21-30	66000	not-purchased
65	15583681	Male	32	31-40	120000	purchased
66	15605000	Female	59	51-60	83000	not-purchased
67	15718071	Male	24	21-30	58000	not-purchased
68	15679760	Male	24	21-30	19000	not-purchased
69	15654574	Female	23	21-30	82000	not-purchased
70	15577178	Female	22	21-30	63000	not-purchased
71	15595324	Female	31	31-40	68000	not-purchased
72	15756932	Male	25	21-30	80000	not-purchased
73	15726358	Female	24	21-30	27000	not-purchased
74	15595228	Female	20	18-20	23000	not-purchased
75	15782530	Female	33	31-40	113000	not-purchased
76	15592877	Male	32	31-40	18000	not-purchased
77	15651983	Male	34	31-40	112000	purchased
78	15746737	Male	18	18-20	52000	not-purchased
79	15774179	Female	22	21-30	27000	not-purchased
80	15667265	Female	28	21-30	87000	not-purchased
81	15655123	Female	26	21-30	17000	not-purchased
82	15595917	Male	30	21-30	80000	not-purchased
83	15668385	Male	39	31-40	42000	not-purchased
84	15709476	Male	20	18-20	49000	not-purchased
85	15711218	Male	35	31-40	88000	not-purchased
86	15798659	Female	30	21-30	62000	not-purchased
87	15663939	Female	31	31-40	118000	purchased
88	15694946	Male	24	21-30	55000	not-purchased
89	15631912	Female	28	21-30	85000	not-purchased
90	15768816	Male	26	21-30	81000	not-purchased
91	15682268	Male	35	31-40	50000	not-purchased
92	15684801	Male	22	21-30	81000	not-purchased
93	15636428	Female	30	21-30	116000	not-purchased
94	15809823	Male	26	21-30	15000	not-purchased
95	15699284	Female	29	21-30	28000	not-purchased
96	15786993	Female	29	21-30	83000	not-purchased
97	15709441	Female	35	31-40	44000	not-purchased
98	15710257	Female	35	31-40	25000	not-purchased
99	15582492	Male	28	21-30	123000	purchased
100	15575694	Male	35	31-40	73000	not-purchased