electricsheepafrica/africa-world-bank-public-sector-indicators-for-cameroon

Cameroon - Public Sector

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.

Effective governments improve people's standard of living by ensuring access to essential services – health, education, water and sanitation, electricity, transport – and the opportunity to live and work in peace and security. Data here includes World Bank staff assessments of country performance in economic management, structural policies, policies for social inclusion and equity, and public sector management and institutions for the poorest countries. Also included are indicators on revenues and expenses from the International Monetary Fund's Government Finance Statistics, and on tax policies from various sources.

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	Public health
Unit of observation	Country-level aggregates
Rows (total)	2,597
Columns	8 (2 numeric, 6 categorical, 0 datetime)
Train split	2,077 rows
Test split	519 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 1961.0–2024.0).

Outcome / Measurement — value (range -1035800000000.0–5763760000000.0).

Identifier / Metadata — indicator_name (Military expenditure (current USD), Military expenditure (% of GDP), Military expenditure (current LCU)), indicator_code (MS.MIL.XPND.CD, MS.MIL.XPND.GD.ZS, MS.MIL.XPND.CN), esa_source (HDX), esa_processed (2026-04-14).

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-world-bank-public-sector-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%	1961.0 – 2024.0 (mean 2009.2018)
indicator_name	object	0.0%	Military expenditure (current USD), Military expenditure (% of GDP), Military expenditure (current LCU)
indicator_code	object	0.0%	MS.MIL.XPND.CD, MS.MIL.XPND.GD.ZS, MS.MIL.XPND.CN
value	float64	0.0%	-1035800000000.0 – 5763760000000.0 (mean 80225297647.7526)
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-14

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Numeric Summary

Column	Min	Max	Mean	Median
year	1961.0	2024.0	2009.2018	2012.0
value	-1035800000000.0	5763760000000.0	80225297647.7526	10.9453

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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_public_sector_indicators_for_cameroon,
  title     = {Cameroon - Public Sector},
  author    = {World Bank Group},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/world-bank-public-sector-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.