Vehicle-Anti-Theft-Face-Recognition-System/venv/Lib/site-packages/sklearn/datasets/data/iris.csv

1	150	4	setosa	versicolor	virginica
2	5.1	3.5	1.4	0.2	0
3	4.9	3.0	1.4	0.2	0
4	4.7	3.2	1.3	0.2	0
5	4.6	3.1	1.5	0.2	0
6	5.0	3.6	1.4	0.2	0
7	5.4	3.9	1.7	0.4	0
8	4.6	3.4	1.4	0.3	0
9	5.0	3.4	1.5	0.2	0
10	4.4	2.9	1.4	0.2	0
11	4.9	3.1	1.5	0.1	0
12	5.4	3.7	1.5	0.2	0
13	4.8	3.4	1.6	0.2	0
14	4.8	3.0	1.4	0.1	0
15	4.3	3.0	1.1	0.1	0
16	5.8	4.0	1.2	0.2	0
17	5.7	4.4	1.5	0.4	0
18	5.4	3.9	1.3	0.4	0
19	5.1	3.5	1.4	0.3	0
20	5.7	3.8	1.7	0.3	0
21	5.1	3.8	1.5	0.3	0
22	5.4	3.4	1.7	0.2	0
23	5.1	3.7	1.5	0.4	0
24	4.6	3.6	1.0	0.2	0
25	5.1	3.3	1.7	0.5	0
26	4.8	3.4	1.9	0.2	0
27	5.0	3.0	1.6	0.2	0
28	5.0	3.4	1.6	0.4	0
29	5.2	3.5	1.5	0.2	0
30	5.2	3.4	1.4	0.2	0
31	4.7	3.2	1.6	0.2	0
32	4.8	3.1	1.6	0.2	0
33	5.4	3.4	1.5	0.4	0
34	5.2	4.1	1.5	0.1	0
35	5.5	4.2	1.4	0.2	0
36	4.9	3.1	1.5	0.2	0
37	5.0	3.2	1.2	0.2	0
38	5.5	3.5	1.3	0.2	0
39	4.9	3.6	1.4	0.1	0
40	4.4	3.0	1.3	0.2	0
41	5.1	3.4	1.5	0.2	0
42	5.0	3.5	1.3	0.3	0
43	4.5	2.3	1.3	0.3	0
44	4.4	3.2	1.3	0.2	0
45	5.0	3.5	1.6	0.6	0
46	5.1	3.8	1.9	0.4	0
47	4.8	3.0	1.4	0.3	0
48	5.1	3.8	1.6	0.2	0
49	4.6	3.2	1.4	0.2	0
50	5.3	3.7	1.5	0.2	0
51	5.0	3.3	1.4	0.2	0
52	7.0	3.2	4.7	1.4	1
53	6.4	3.2	4.5	1.5	1
54	6.9	3.1	4.9	1.5	1
55	5.5	2.3	4.0	1.3	1
56	6.5	2.8	4.6	1.5	1
57	5.7	2.8	4.5	1.3	1
58	6.3	3.3	4.7	1.6	1
59	4.9	2.4	3.3	1.0	1
60	6.6	2.9	4.6	1.3	1
61	5.2	2.7	3.9	1.4	1
62	5.0	2.0	3.5	1.0	1
63	5.9	3.0	4.2	1.5	1
64	6.0	2.2	4.0	1.0	1
65	6.1	2.9	4.7	1.4	1
66	5.6	2.9	3.6	1.3	1
67	6.7	3.1	4.4	1.4	1
68	5.6	3.0	4.5	1.5	1
69	5.8	2.7	4.1	1.0	1
70	6.2	2.2	4.5	1.5	1
71	5.6	2.5	3.9	1.1	1
72	5.9	3.2	4.8	1.8	1
73	6.1	2.8	4.0	1.3	1
74	6.3	2.5	4.9	1.5	1
75	6.1	2.8	4.7	1.2	1
76	6.4	2.9	4.3	1.3	1
77	6.6	3.0	4.4	1.4	1
78	6.8	2.8	4.8	1.4	1
79	6.7	3.0	5.0	1.7	1
80	6.0	2.9	4.5	1.5	1
81	5.7	2.6	3.5	1.0	1
82	5.5	2.4	3.8	1.1	1
83	5.5	2.4	3.7	1.0	1
84	5.8	2.7	3.9	1.2	1
85	6.0	2.7	5.1	1.6	1
86	5.4	3.0	4.5	1.5	1
87	6.0	3.4	4.5	1.6	1
88	6.7	3.1	4.7	1.5	1
89	6.3	2.3	4.4	1.3	1
90	5.6	3.0	4.1	1.3	1
91	5.5	2.5	4.0	1.3	1
92	5.5	2.6	4.4	1.2	1
93	6.1	3.0	4.6	1.4	1
94	5.8	2.6	4.0	1.2	1
95	5.0	2.3	3.3	1.0	1
96	5.6	2.7	4.2	1.3	1
97	5.7	3.0	4.2	1.2	1
98	5.7	2.9	4.2	1.3	1
99	6.2	2.9	4.3	1.3	1
100	5.1	2.5	3.0	1.1	1
101	5.7	2.8	4.1	1.3	1
102	6.3	3.3	6.0	2.5	2
103	5.8	2.7	5.1	1.9	2
104	7.1	3.0	5.9	2.1	2
105	6.3	2.9	5.6	1.8	2
106	6.5	3.0	5.8	2.2	2
107	7.6	3.0	6.6	2.1	2
108	4.9	2.5	4.5	1.7	2
109	7.3	2.9	6.3	1.8	2
110	6.7	2.5	5.8	1.8	2
111	7.2	3.6	6.1	2.5	2
112	6.5	3.2	5.1	2.0	2
113	6.4	2.7	5.3	1.9	2
114	6.8	3.0	5.5	2.1	2
115	5.7	2.5	5.0	2.0	2
116	5.8	2.8	5.1	2.4	2
117	6.4	3.2	5.3	2.3	2
118	6.5	3.0	5.5	1.8	2
119	7.7	3.8	6.7	2.2	2
120	7.7	2.6	6.9	2.3	2
121	6.0	2.2	5.0	1.5	2
122	6.9	3.2	5.7	2.3	2
123	5.6	2.8	4.9	2.0	2
124	7.7	2.8	6.7	2.0	2
125	6.3	2.7	4.9	1.8	2
126	6.7	3.3	5.7	2.1	2
127	7.2	3.2	6.0	1.8	2
128	6.2	2.8	4.8	1.8	2
129	6.1	3.0	4.9	1.8	2
130	6.4	2.8	5.6	2.1	2
131	7.2	3.0	5.8	1.6	2
132	7.4	2.8	6.1	1.9	2
133	7.9	3.8	6.4	2.0	2
134	6.4	2.8	5.6	2.2	2
135	6.3	2.8	5.1	1.5	2
136	6.1	2.6	5.6	1.4	2
137	7.7	3.0	6.1	2.3	2
138	6.3	3.4	5.6	2.4	2
139	6.4	3.1	5.5	1.8	2
140	6.0	3.0	4.8	1.8	2
141	6.9	3.1	5.4	2.1	2
142	6.7	3.1	5.6	2.4	2
143	6.9	3.1	5.1	2.3	2
144	5.8	2.7	5.1	1.9	2
145	6.8	3.2	5.9	2.3	2
146	6.7	3.3	5.7	2.5	2
147	6.7	3.0	5.2	2.3	2
148	6.3	2.5	5.0	1.9	2
149	6.5	3.0	5.2	2.0	2
150	6.2	3.4	5.4	2.3	2
151	5.9	3.0	5.1	1.8	2