electricsheepafrica/africa-world-bank-education-indicators-for-cameroon

Cameroon - Education

Publisher: World Bank Group · Source: HDX · License: cc-by · Updated: 2026-03-27

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Abstract

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Education is one of the most powerful instruments for reducing poverty and inequality and lays a foundation for sustained economic growth. The World Bank compiles data on education inputs, participation, efficiency, and outcomes. Data on education are compiled by the United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics from official responses to surveys and from reports provided by education authorities in each country.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: CMR.

Curated into ML-ready Parquet format by Electric Sheep Africa.

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Dataset Characteristics

Domain	Education
Unit of observation	Country-level aggregates
Rows (total)	16,696
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	13,356 rows
Test split	3,339 rows
Geographic scope	CMR
Publisher	World Bank Group
HDX last updated	2026-03-27

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Variables

Geographic — country_name (Cameroon), country_iso3 (CMR), year (range 1960.0–2025.0).

Outcome / Measurement — value (range 0.0–11466010.0).

Identifier / Metadata — indicator_name (Population ages 15-64 (% of total population), Population ages 0-14 (% of total population), Number of under-five deaths, male), indicator_code (SP.POP.1564.TO.ZS, SP.POP.0014.TO.ZS, SH.DTH.MORT.MA), esa_source (HDX), esa_processed (2026-04-15).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-education-indicators-for-cameroon")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

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Schema

Column	Type	Null %	Range / Sample Values
country_name	object	0.0%	Cameroon
country_iso3	object	0.0%	CMR
year	int64	0.0%	1960.0 – 2025.0 (mean 1996.145)
indicator_name	object	0.0%	Population ages 15-64 (% of total population), Population ages 0-14 (% of total population), Number of under-five deaths, male
indicator_code	object	0.0%	SP.POP.1564.TO.ZS, SP.POP.0014.TO.ZS, SH.DTH.MORT.MA
value	float64	0.0%	0.0 – 11466010.0 (mean 392528.7278)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-15

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Numeric Summary

Column	Min	Max	Mean	Median
year	1960.0	2025.0	1996.145	1999.0
value	0.0	11466010.0	392528.7278	205.5

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Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

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Limitations

• Data originates from World Bank Group and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_world_bank_education_indicators_for_cameroon,
  title     = {Cameroon - Education},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-education-indicators-for-cameroon},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

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Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.