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lvwerra
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red-wine

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train · 1.6k rows

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fixed acidity float64 4.6 15.9	volatile acidity float64 0.12 1.58	citric acid float64 0 1	residual sugar float64 0.9 15.5	chlorides float64 0.01 0.61	free sulfur dioxide float64 1 72	total sulfur dioxide float64 6 289	density float64 0.99 1	pH float64 2.74 4.01	sulphates float64 0.33 2	alcohol float64 8.4 14.9	quality int64 3 8
7.4	0.7	0	1.9	0.076	11	34	0.9978	3.51	0.56	9.4	5
7.8	0.88	0	2.6	0.098	25	67	0.9968	3.2	0.68	9.8	5
7.8	0.76	0.04	2.3	0.092	15	54	0.997	3.26	0.65	9.8	5
11.2	0.28	0.56	1.9	0.075	17	60	0.998	3.16	0.58	9.8	6
7.4	0.7	0	1.9	0.076	11	34	0.9978	3.51	0.56	9.4	5
7.4	0.66	0	1.8	0.075	13	40	0.9978	3.51	0.56	9.4	5
7.9	0.6	0.06	1.6	0.069	15	59	0.9964	3.3	0.46	9.4	5
7.3	0.65	0	1.2	0.065	15	21	0.9946	3.39	0.47	10	7
7.8	0.58	0.02	2	0.073	9	18	0.9968	3.36	0.57	9.5	7
7.5	0.5	0.36	6.1	0.071	17	102	0.9978	3.35	0.8	10.5	5
6.7	0.58	0.08	1.8	0.097	15	65	0.9959	3.28	0.54	9.2	5
7.5	0.5	0.36	6.1	0.071	17	102	0.9978	3.35	0.8	10.5	5
5.6	0.615	0	1.6	0.089	16	59	0.9943	3.58	0.52	9.9	5
7.8	0.61	0.29	1.6	0.114	9	29	0.9974	3.26	1.56	9.1	5
8.9	0.62	0.18	3.8	0.176	52	145	0.9986	3.16	0.88	9.2	5
8.9	0.62	0.19	3.9	0.17	51	148	0.9986	3.17	0.93	9.2	5
8.5	0.28	0.56	1.8	0.092	35	103	0.9969	3.3	0.75	10.5	7
8.1	0.56	0.28	1.7	0.368	16	56	0.9968	3.11	1.28	9.3	5
7.4	0.59	0.08	4.4	0.086	6	29	0.9974	3.38	0.5	9	4
7.9	0.32	0.51	1.8	0.341	17	56	0.9969	3.04	1.08	9.2	6
8.9	0.22	0.48	1.8	0.077	29	60	0.9968	3.39	0.53	9.4	6
7.6	0.39	0.31	2.3	0.082	23	71	0.9982	3.52	0.65	9.7	5
7.9	0.43	0.21	1.6	0.106	10	37	0.9966	3.17	0.91	9.5	5
8.5	0.49	0.11	2.3	0.084	9	67	0.9968	3.17	0.53	9.4	5
6.9	0.4	0.14	2.4	0.085	21	40	0.9968	3.43	0.63	9.7	6
6.3	0.39	0.16	1.4	0.08	11	23	0.9955	3.34	0.56	9.3	5
7.6	0.41	0.24	1.8	0.08	4	11	0.9962	3.28	0.59	9.5	5
7.9	0.43	0.21	1.6	0.106	10	37	0.9966	3.17	0.91	9.5	5
7.1	0.71	0	1.9	0.08	14	35	0.9972	3.47	0.55	9.4	5
7.8	0.645	0	2	0.082	8	16	0.9964	3.38	0.59	9.8	6
6.7	0.675	0.07	2.4	0.089	17	82	0.9958	3.35	0.54	10.1	5
6.9	0.685	0	2.5	0.105	22	37	0.9966	3.46	0.57	10.6	6
8.3	0.655	0.12	2.3	0.083	15	113	0.9966	3.17	0.66	9.8	5
6.9	0.605	0.12	10.7	0.073	40	83	0.9993	3.45	0.52	9.4	6
5.2	0.32	0.25	1.8	0.103	13	50	0.9957	3.38	0.55	9.2	5
7.8	0.645	0	5.5	0.086	5	18	0.9986	3.4	0.55	9.6	6
7.8	0.6	0.14	2.4	0.086	3	15	0.9975	3.42	0.6	10.8	6
8.1	0.38	0.28	2.1	0.066	13	30	0.9968	3.23	0.73	9.7	7
5.7	1.13	0.09	1.5	0.172	7	19	0.994	3.5	0.48	9.8	4
7.3	0.45	0.36	5.9	0.074	12	87	0.9978	3.33	0.83	10.5	5
7.3	0.45	0.36	5.9	0.074	12	87	0.9978	3.33	0.83	10.5	5
8.8	0.61	0.3	2.8	0.088	17	46	0.9976	3.26	0.51	9.3	4
7.5	0.49	0.2	2.6	0.332	8	14	0.9968	3.21	0.9	10.5	6
8.1	0.66	0.22	2.2	0.069	9	23	0.9968	3.3	1.2	10.3	5
6.8	0.67	0.02	1.8	0.05	5	11	0.9962	3.48	0.52	9.5	5
4.6	0.52	0.15	2.1	0.054	8	65	0.9934	3.9	0.56	13.1	4
7.7	0.935	0.43	2.2	0.114	22	114	0.997	3.25	0.73	9.2	5
8.7	0.29	0.52	1.6	0.113	12	37	0.9969	3.25	0.58	9.5	5
6.4	0.4	0.23	1.6	0.066	5	12	0.9958	3.34	0.56	9.2	5
5.6	0.31	0.37	1.4	0.074	12	96	0.9954	3.32	0.58	9.2	5
8.8	0.66	0.26	1.7	0.074	4	23	0.9971	3.15	0.74	9.2	5
6.6	0.52	0.04	2.2	0.069	8	15	0.9956	3.4	0.63	9.4	6
6.6	0.5	0.04	2.1	0.068	6	14	0.9955	3.39	0.64	9.4	6
8.6	0.38	0.36	3	0.081	30	119	0.997	3.2	0.56	9.4	5
7.6	0.51	0.15	2.8	0.11	33	73	0.9955	3.17	0.63	10.2	6
7.7	0.62	0.04	3.8	0.084	25	45	0.9978	3.34	0.53	9.5	5
10.2	0.42	0.57	3.4	0.07	4	10	0.9971	3.04	0.63	9.6	5
7.5	0.63	0.12	5.1	0.111	50	110	0.9983	3.26	0.77	9.4	5
7.8	0.59	0.18	2.3	0.076	17	54	0.9975	3.43	0.59	10	5
7.3	0.39	0.31	2.4	0.074	9	46	0.9962	3.41	0.54	9.4	6
8.8	0.4	0.4	2.2	0.079	19	52	0.998	3.44	0.64	9.2	5
7.7	0.69	0.49	1.8	0.115	20	112	0.9968	3.21	0.71	9.3	5
7.5	0.52	0.16	1.9	0.085	12	35	0.9968	3.38	0.62	9.5	7
7	0.735	0.05	2	0.081	13	54	0.9966	3.39	0.57	9.8	5
7.2	0.725	0.05	4.65	0.086	4	11	0.9962	3.41	0.39	10.9	5
7.2	0.725	0.05	4.65	0.086	4	11	0.9962	3.41	0.39	10.9	5
7.5	0.52	0.11	1.5	0.079	11	39	0.9968	3.42	0.58	9.6	5
6.6	0.705	0.07	1.6	0.076	6	15	0.9962	3.44	0.58	10.7	5
9.3	0.32	0.57	2	0.074	27	65	0.9969	3.28	0.79	10.7	5
8	0.705	0.05	1.9	0.074	8	19	0.9962	3.34	0.95	10.5	6
7.7	0.63	0.08	1.9	0.076	15	27	0.9967	3.32	0.54	9.5	6
7.7	0.67	0.23	2.1	0.088	17	96	0.9962	3.32	0.48	9.5	5
7.7	0.69	0.22	1.9	0.084	18	94	0.9961	3.31	0.48	9.5	5
8.3	0.675	0.26	2.1	0.084	11	43	0.9976	3.31	0.53	9.2	4
9.7	0.32	0.54	2.5	0.094	28	83	0.9984	3.28	0.82	9.6	5
8.8	0.41	0.64	2.2	0.093	9	42	0.9986	3.54	0.66	10.5	5
8.8	0.41	0.64	2.2	0.093	9	42	0.9986	3.54	0.66	10.5	5
6.8	0.785	0	2.4	0.104	14	30	0.9966	3.52	0.55	10.7	6
6.7	0.75	0.12	2	0.086	12	80	0.9958	3.38	0.52	10.1	5
8.3	0.625	0.2	1.5	0.08	27	119	0.9972	3.16	1.12	9.1	4
6.2	0.45	0.2	1.6	0.069	3	15	0.9958	3.41	0.56	9.2	5
7.8	0.43	0.7	1.9	0.464	22	67	0.9974	3.13	1.28	9.4	5
7.4	0.5	0.47	2	0.086	21	73	0.997	3.36	0.57	9.1	5
7.3	0.67	0.26	1.8	0.401	16	51	0.9969	3.16	1.14	9.4	5
6.3	0.3	0.48	1.8	0.069	18	61	0.9959	3.44	0.78	10.3	6
6.9	0.55	0.15	2.2	0.076	19	40	0.9961	3.41	0.59	10.1	5
8.6	0.49	0.28	1.9	0.11	20	136	0.9972	2.93	1.95	9.9	6
7.7	0.49	0.26	1.9	0.062	9	31	0.9966	3.39	0.64	9.6	5
9.3	0.39	0.44	2.1	0.107	34	125	0.9978	3.14	1.22	9.5	5
7	0.62	0.08	1.8	0.076	8	24	0.9978	3.48	0.53	9	5
7.9	0.52	0.26	1.9	0.079	42	140	0.9964	3.23	0.54	9.5	5
8.6	0.49	0.28	1.9	0.11	20	136	0.9972	2.93	1.95	9.9	6
8.6	0.49	0.29	2	0.11	19	133	0.9972	2.93	1.98	9.8	5
7.7	0.49	0.26	1.9	0.062	9	31	0.9966	3.39	0.64	9.6	5
5	1.02	0.04	1.4	0.045	41	85	0.9938	3.75	0.48	10.5	4
4.7	0.6	0.17	2.3	0.058	17	106	0.9932	3.85	0.6	12.9	6
6.8	0.775	0	3	0.102	8	23	0.9965	3.45	0.56	10.7	5
7	0.5	0.25	2	0.07	3	22	0.9963	3.25	0.63	9.2	5
7.6	0.9	0.06	2.5	0.079	5	10	0.9967	3.39	0.56	9.8	5
8.1	0.545	0.18	1.9	0.08	13	35	0.9972	3.3	0.59	9	6

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Red Wine Dataset 🍷

This dataset contains the red wine dataset found here. See also this example of a Scikit-Learn model trained on this dataset.

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Size of downloaded dataset files:

92 kB

Size of the auto-converted Parquet files:

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Number of rows:

1,599

Models trained or fine-tuned on lvwerra/red-wine