Datasets:

mstz
/

pima

number_of_pregnancies int8 0 17	plasma_glucose_concentration float64 0 199	diastolic_blood_pressure float64 0 122	triceps_thickness float64 0 99	serum_insulin float64 0 846	bmi float64 0 67.1	diabetes_pedigree float64 0.08 2.42	age float64 21 81	has_diabetes class label 2 classes
6	148	72	35	0	33.6	0.627	50	1yes
1	85	66	29	0	26.6	0.351	31	0no
8	183	64	0	0	23.3	0.672	32	1yes
1	89	66	23	94	28.1	0.167	21	0no
0	137	40	35	168	43.1	2.288	33	1yes
5	116	74	0	0	25.6	0.201	30	0no
3	78	50	32	88	31	0.248	26	1yes
10	115	0	0	0	35.3	0.134	29	0no
2	197	70	45	543	30.5	0.158	53	1yes
8	125	96	0	0	0	0.232	54	1yes
4	110	92	0	0	37.6	0.191	30	0no
10	168	74	0	0	38	0.537	34	1yes
10	139	80	0	0	27.1	1.441	57	0no
1	189	60	23	846	30.1	0.398	59	1yes
5	166	72	19	175	25.8	0.587	51	1yes
7	100	0	0	0	30	0.484	32	1yes
0	118	84	47	230	45.8	0.551	31	1yes
7	107	74	0	0	29.6	0.254	31	1yes
1	103	30	38	83	43.3	0.183	33	0no
1	115	70	30	96	34.6	0.529	32	1yes
3	126	88	41	235	39.3	0.704	27	0no
8	99	84	0	0	35.4	0.388	50	0no
7	196	90	0	0	39.8	0.451	41	1yes
9	119	80	35	0	29	0.263	29	1yes
11	143	94	33	146	36.6	0.254	51	1yes
10	125	70	26	115	31.1	0.205	41	1yes
7	147	76	0	0	39.4	0.257	43	1yes
1	97	66	15	140	23.2	0.487	22	0no
13	145	82	19	110	22.2	0.245	57	0no
5	117	92	0	0	34.1	0.337	38	0no
5	109	75	26	0	36	0.546	60	0no
3	158	76	36	245	31.6	0.851	28	1yes
3	88	58	11	54	24.8	0.267	22	0no
6	92	92	0	0	19.9	0.188	28	0no
10	122	78	31	0	27.6	0.512	45	0no
4	103	60	33	192	24	0.966	33	0no
11	138	76	0	0	33.2	0.42	35	0no
9	102	76	37	0	32.9	0.665	46	1yes
2	90	68	42	0	38.2	0.503	27	1yes
4	111	72	47	207	37.1	1.39	56	1yes
3	180	64	25	70	34	0.271	26	0no
7	133	84	0	0	40.2	0.696	37	0no
7	106	92	18	0	22.7	0.235	48	0no
9	171	110	24	240	45.4	0.721	54	1yes
7	159	64	0	0	27.4	0.294	40	0no
0	180	66	39	0	42	1.893	25	1yes
1	146	56	0	0	29.7	0.564	29	0no
2	71	70	27	0	28	0.586	22	0no
7	103	66	32	0	39.1	0.344	31	1yes
7	105	0	0	0	0	0.305	24	0no
1	103	80	11	82	19.4	0.491	22	0no
1	101	50	15	36	24.2	0.526	26	0no
5	88	66	21	23	24.4	0.342	30	0no
8	176	90	34	300	33.7	0.467	58	1yes
7	150	66	42	342	34.7	0.718	42	0no
1	73	50	10	0	23	0.248	21	0no
7	187	68	39	304	37.7	0.254	41	1yes
0	100	88	60	110	46.8	0.962	31	0no
0	146	82	0	0	40.5	1.781	44	0no
0	105	64	41	142	41.5	0.173	22	0no
2	84	0	0	0	0	0.304	21	0no
8	133	72	0	0	32.9	0.27	39	1yes
5	44	62	0	0	25	0.587	36	0no
2	141	58	34	128	25.4	0.699	24	0no
7	114	66	0	0	32.8	0.258	42	1yes
5	99	74	27	0	29	0.203	32	0no
0	109	88	30	0	32.5	0.855	38	1yes
2	109	92	0	0	42.7	0.845	54	0no
1	95	66	13	38	19.6	0.334	25	0no
4	146	85	27	100	28.9	0.189	27	0no
2	100	66	20	90	32.9	0.867	28	1yes
5	139	64	35	140	28.6	0.411	26	0no
13	126	90	0	0	43.4	0.583	42	1yes
4	129	86	20	270	35.1	0.231	23	0no
1	79	75	30	0	32	0.396	22	0no
1	0	48	20	0	24.7	0.14	22	0no
7	62	78	0	0	32.6	0.391	41	0no
5	95	72	33	0	37.7	0.37	27	0no
0	131	0	0	0	43.2	0.27	26	1yes
2	112	66	22	0	25	0.307	24	0no
3	113	44	13	0	22.4	0.14	22	0no
2	74	0	0	0	0	0.102	22	0no
7	83	78	26	71	29.3	0.767	36	0no
0	101	65	28	0	24.6	0.237	22	0no
5	137	108	0	0	48.8	0.227	37	1yes
2	110	74	29	125	32.4	0.698	27	0no
13	106	72	54	0	36.6	0.178	45	0no
2	100	68	25	71	38.5	0.324	26	0no
15	136	70	32	110	37.1	0.153	43	1yes
1	107	68	19	0	26.5	0.165	24	0no
1	80	55	0	0	19.1	0.258	21	0no
4	123	80	15	176	32	0.443	34	0no
7	81	78	40	48	46.7	0.261	42	0no
4	134	72	0	0	23.8	0.277	60	1yes
2	142	82	18	64	24.7	0.761	21	0no
6	144	72	27	228	33.9	0.255	40	0no
2	92	62	28	0	31.6	0.13	24	0no
1	71	48	18	76	20.4	0.323	22	0no
6	93	50	30	64	28.7	0.356	23	0no
1	122	90	51	220	49.7	0.325	31	1yes

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pima

The pima dataset from the UCI ML repository. Predict diabetes of a patient.

Configurations and tasks

Configuration	Task	Description
pima	Binary classification	Does the patient have diabetes?

Usage

from datasets import load_dataset

dataset = load_dataset("mstz/pima")["train"]

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Size of downloaded dataset files:

23.1 kB

Size of the auto-converted Parquet files:

16.4 kB

Number of rows:

768