Datasets:

electricsheepafrica
/

africa-synth-malaria-smoking-health-nigeria

age int64	sex string	region string	location_type string	current_smoker string	cigs_per_day int64	sickle_cell_genotype string	malaria_exposure string	hemoglobin_g_per_dL float64	heart_rate_bpm int64	blood_pressure string	cholesterol_mg_per_dL float64
22	male	South_West	urban	no	0	AA	chronic	14.5	70	119/60	254.3
22	female	North_West	urban	no	0	AA	rare	14.5	69	141/91	217.8
53	female	North_East	rural	no	0	AS	rare	13.9	79	122/62	197.4
40	male	North_West	rural	no	0	AS	rare	14.4	67	122/81	197.5
29	male	South_East	rural	no	0	AA	rare	14.8	52	107/61	155.8
29	female	North_West	rural	no	0	AA	rare	12.4	61	107/77	208.9
42	female	South_East	urban	no	0	AA	recent	10.5	80	113/79	287.3
35	male	South_East	rural	no	0	AA	recent	11.4	102	109/73	161.3
70	male	North_West	rural	no	0	AA	rare	13.4	73	130/90	190.4
70	male	South_West	rural	no	0	AA	chronic	13	65	135/88	216.5
70	female	North_West	urban	no	0	SS	rare	6.3	118	127/87	249.5
36	male	North_East	rural	no	0	AA	chronic	12.6	80	118/78	171.1
49	female	North_Central	urban	no	0	AS	recent	10.1	74	125/63	193
25	male	South_West	urban	no	0	AA	rare	14.7	79	130/82	242.8
25	male	North_West	rural	no	0	AA	rare	14.4	68	109/73	160.1
22	female	South_South	rural	no	0	AA	rare	12.3	67	105/61	227.7
47	male	North_West	rural	no	0	AA	chronic	14.3	54	118/76	221.4
23	female	South_East	rural	no	0	AA	chronic	11.7	80	102/77	214.9
28	male	North_West	rural	no	0	AS	rare	15	81	120/76	173.9
57	female	South_East	rural	no	0	AA	chronic	11.8	87	116/75	242.8
69	female	North_Central	rural	no	0	AA	recent	9.2	106	103/62	186.3
31	male	South_West	urban	no	0	AS	rare	12.4	80	107/81	279
67	female	North_West	rural	no	0	AS	recent	9.2	124	125/67	229.3
19	female	North_West	urban	no	0	AA	rare	12.4	75	100/81	278.3
30	male	South_East	rural	no	0	SS	rare	8	123	101/61	127.4
41	female	North_East	urban	no	0	AA	recent	11.7	101	123/81	225
27	male	South_East	rural	no	0	AA	rare	14.6	52	109/77	193.2
42	female	North_East	rural	no	0	AA	recent	11.4	111	114/74	190.5
44	female	North_East	rural	no	0	AA	rare	12.2	66	126/67	234.5
21	female	South_West	rural	no	0	AS	rare	12.7	66	122/82	210.9
29	female	North_Central	rural	no	0	AS	chronic	11.1	93	117/67	178.4
33	male	North_Central	rural	no	0	AA	rare	14.1	68	117/74	166.6
29	female	South_East	rural	no	0	AA	rare	13.3	74	110/70	199.7
21	male	South_West	urban	no	0	AA	chronic	12.9	57	124/78	220.2
55	male	South_West	urban	no	0	AS	rare	13.4	63	117/79	245.8
24	female	South_East	rural	no	0	AA	rare	13.4	90	117/60	202.2
36	female	South_South	rural	no	0	AA	chronic	12.4	71	108/73	145.4
53	female	South_South	rural	no	0	AA	rare	13	72	107/66	285.4
39	male	North_Central	rural	no	0	AA	chronic	13.2	75	103/65	213.1
40	male	North_East	urban	no	0	AS	rare	12.4	83	133/81	264.8
37	female	South_East	rural	no	0	AA	rare	13.8	62	122/64	245.6
22	female	North_East	rural	no	0	AS	chronic	10.3	92	114/67	261.7
42	female	South_South	urban	no	0	AA	recent	10.1	96	116/66	239.5
24	male	North_West	rural	no	0	AA	chronic	13.2	56	109/81	204.9
41	female	South_South	urban	no	0	AA	recent	9.4	112	122/65	203.3
25	male	North_Central	rural	no	0	SS	chronic	8.1	99	143/89	197.3
18	female	South_South	urban	no	0	AA	chronic	12.5	59	102/78	232
22	male	South_West	rural	no	0	AS	rare	15.3	55	114/69	210.1
35	female	North_East	urban	no	0	AA	chronic	12.5	79	105/60	189.2
35	female	South_West	urban	no	0	AS	rare	11.1	84	118/81	242.9
34	male	South_West	urban	no	0	AA	rare	14.8	75	109/74	209.4
40	female	South_South	urban	yes	6	AA	recent	11.4	116	167/117	210
33	female	South_West	urban	no	0	AA	rare	14.9	76	121/63	189.8
33	male	North_East	rural	no	0	AA	chronic	13.8	62	116/60	231.7
29	female	North_Central	rural	no	0	AA	chronic	13.4	63	115/63	228.4
65	male	South_West	urban	no	0	AA	rare	14.5	80	176/104	319
33	female	South_South	urban	no	0	AA	chronic	12.2	64	127/73	200.4
64	female	South_East	urban	no	0	SS	chronic	6	107	144/87	258.9
24	female	North_West	rural	no	0	AA	rare	13.3	80	115/61	197.4
35	male	North_West	rural	no	0	AA	rare	16.2	69	104/62	288.8
32	female	North_West	rural	no	0	AA	rare	13.6	63	127/77	168.9
39	male	North_West	rural	no	0	AA	rare	15.1	69	128/60	226.4
31	male	South_West	urban	no	0	AA	rare	15.1	71	121/67	205.8
22	male	North_Central	rural	no	0	AA	chronic	11.2	82	113/82	224.2
24	female	South_West	urban	no	0	AA	chronic	11.9	68	148/114	233.1
50	female	South_West	rural	no	0	AA	rare	13.4	63	121/64	214.6
66	male	North_West	urban	no	0	AA	rare	14.1	60	168/111	265.5
50	female	North_East	rural	no	0	AS	chronic	11.6	90	121/72	241.6
24	female	South_South	rural	no	0	AA	rare	11.6	77	130/90	212.5
43	male	South_South	urban	no	0	AA	chronic	13.7	77	143/84	380.2
55	male	South_West	urban	no	0	AS	chronic	13.2	83	167/96	279.9
69	male	North_Central	rural	no	0	AA	chronic	14.1	68	158/113	244.4
28	male	North_West	urban	no	0	AA	rare	13.6	69	137/90	212.2
24	female	North_West	rural	no	0	AA	recent	10.8	120	127/81	141
26	female	North_West	rural	no	0	AA	rare	13.7	55	104/62	183.8
35	male	North_East	rural	no	0	AA	recent	13.3	91	124/77	177.4
29	male	North_West	rural	no	0	AA	rare	13.6	78	121/75	178.3
19	male	South_West	urban	no	0	AA	rare	14.8	72	124/73	210.4
65	male	South_West	urban	no	0	AA	rare	16.5	78	137/93	185.1
40	male	South_East	rural	no	0	AA	chronic	13.6	65	146/97	238
33	male	South_East	rural	no	0	AA	recent	12	93	108/74	223.3
42	female	North_West	urban	no	0	AA	rare	13.9	59	101/67	224.3
56	female	South_East	urban	no	0	AA	chronic	11.3	72	177/112	304.8
18	female	South_South	urban	no	0	AA	rare	13.4	59	112/60	256.1
25	female	North_East	urban	no	0	AA	rare	15.1	82	109/70	174.3
28	male	North_Central	rural	no	0	AS	rare	13.2	84	104/67	178.8
20	female	North_Central	urban	no	0	AA	rare	13.4	79	128/78	192.6
51	female	South_West	urban	no	0	AA	rare	12.9	50	131/91	204.6
19	female	South_South	rural	no	0	AS	chronic	10.8	71	115/76	243.5
63	male	North_West	rural	no	0	AA	chronic	11.3	77	139/92	224.5
36	female	North_Central	urban	no	0	AA	rare	14.4	57	119/64	273.7
20	female	South_East	rural	no	0	AA	chronic	12	73	104/81	208.9
42	male	South_West	urban	no	0	AA	rare	14.7	65	104/62	241.4
34	male	North_Central	rural	no	0	AA	recent	12.3	90	128/65	229.1
29	male	South_East	rural	no	0	AA	rare	16.4	59	126/66	267.2
30	female	South_South	urban	no	0	AA	rare	11	76	111/77	250.6
44	male	South_West	urban	no	0	AA	chronic	13.4	65	128/71	239
23	female	North_East	rural	no	0	AS	rare	11.9	79	145/84	222
26	female	South_West	urban	no	0	AS	rare	12.7	65	107/69	259.5
24	male	South_West	rural	no	0	AA	chronic	14.2	80	119/71	218.3

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Nigeria Smoking & Health - Evidence-Based Probabilistic Dataset | Africa (Electric Sheep Africa metadata inventory)

Size category: 1K<n<10K - Formats: parquet - Sector: health - Engineered by Electric Sheep Africa

TL;DR

This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.

What This Dataset Covers

Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance.

Dataset context from the existing Hugging Face card: ⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference. Nigerian Smoking & Health Dataset Dataset Description A research-grade synthetic dataset modeling smoking behaviors and health outcomes in Nigeria, using probabilistic relationships derived from peer-reviewed epidemiological studies. This dataset reflects Nigeria-specific disease prevalence, genetic factors, and… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-malaria-smoking-health-nigeria.

Dataset Profile

Field	Value
Hugging Face repo	`electricsheepafrica/africa-synth-malaria-smoking-health-nigeria`
Sector	health
Topic tags	nigeria, healthcare, synthetic-data, smoking, sickle-cell, malaria, epidemiology, probabilistic-modeling, synthetic
Modalities	`tabular`, `text`
Formats	`parquet`
Size category	`1K<n<10K`
Countries	Nigeria
ISO3 coverage	`NGA`
Last modified on HF	`2026-04-14 22:37:25+00:00`
Inventory snapshot	`2026-07-16T16:00:34Z`

How To Read This Dataset

Start from the repository files and the dataset viewer when available.
Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-synth-malaria-smoking-health-nigeria")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
Exact schema, row counts, and source files should be inspected in the repository data files.
Metadata gaps from the inventory: upstream_publisher.
Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

Source context: Electric Sheep Africa metadata inventory
Publisher/source attribution: Public dataset metadata
License: mit
Hugging Face URL: https://huggingface.co/datasets/electricsheepafrica/africa-synth-malaria-smoking-health-nigeria
Inventory retrieved at: 2026-07-16T16:00:34Z

Suggested Analyses

Inspect schema and missingness before modeling.
Profile variables by geography, time, and subgroup columns where present.
Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_africa_synth_malaria_smoking_health_nigeria_2026,
  title        = {Nigeria Smoking & Health - Evidence-Based Probabilistic Dataset | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-malaria-smoking-health-nigeria},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-malaria-smoking-health-nigeria}}
}

License

Released under mit.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.

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