electricsheepafrica/africa-cameroon-uneca-education

CAMEROON - Education indicators, UNECA

Publisher: United Nations Economic Commission for Africa · Source: HDX · License: cc-by-igo · Updated: 2024-09-13

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Abstract

This dataset contains many indicators in education such as as Net enrolment rate in primary education, Ratio of girls to boys in primary education, etc. The whole list and their description can be find in this link https://bit.ly/2NWP6Z1

Each row in this dataset represents tabular records. Data was last updated on HDX on 2024-09-13. Geographic scope: CMR.

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

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Dataset Characteristics

Domain	Education
Unit of observation	Tabular records
Rows (total)	41
Columns	20 (17 numeric, 3 categorical, 0 datetime)
Train split	32 rows
Test split	8 rows
Geographic scope	CMR
Publisher	United Nations Economic Commission for Africa
HDX last updated	2024-09-13

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Variables

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-14).

Other — indicator (Adult literacy rate - Female (%), Net enrolment rate in secondary education - Total (%), Pupil-teacher ratio, secondary education - Total (rate)), 1999 (range 0.2–88.8), 2000 (range 0.2–99.0), 2001 (range 0.2–105.1), 2002 (range 0.2–105.3) and 13 others.

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Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-cameroon-uneca-education")
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
indicator	object	0.0%	Adult literacy rate - Female (%), Net enrolment rate in secondary education - Total (%), Pupil-teacher ratio, secondary education - Total (rate)
1999	float64	53.7%	0.2 – 88.8 (mean 27.5211)
2000	float64	58.5%	0.2 – 99.0 (mean 38.4588)
2001	float64	53.7%	0.2 – 105.1 (mean 32.9)
2002	float64	41.5%	0.2 – 105.3 (mean 29.4458)
2003	float64	36.6%	0.2 – 105.7 (mean 30.8846)
2004	float64	31.7%	0.2 – 110.1 (mean 29.1893)
2005	float64	26.8%	0.2 – 109.2 (mean 27.9533)
2006	float64	22.0%	0.3 – 108.0 (mean 29.0063)
2007	float64	12.2%	0.4 – 108.9 (mean 36.2222)
2008	float64	22.0%	0.4 – 109.3 (mean 35.0094)
2009	float64	24.4%	0.5 – 111.3 (mean 36.3387)
2010	float64	31.7%	0.5 – 113.9 (mean 42.3214)
2011	float64	22.0%	0.5 – 113.3 (mean 32.3438)
2012	float64	48.8%	0.9 – 118.1 (mean 38.7476)
2013	float64	70.7%	3.4 – 98.1 (mean 36.8167)
2014	float64	51.2%	3.7 – 120.1 (mean 41.71)
2015	float64	78.0%	17.5 – 123.5 (mean 79.0778)
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
1999	0.2	88.8	27.5211	22.8
2000	0.2	99.0	38.4588	23.6
2001	0.2	105.1	32.9	19.6
2002	0.2	105.3	29.4458	6.25
2003	0.2	105.7	30.8846	16.0
2004	0.2	110.1	29.1893	11.55
2005	0.2	109.2	27.9533	6.8
2006	0.3	108.0	29.0063	12.05
2007	0.4	108.9	36.2222	33.8
2008	0.4	109.3	35.0094	29.1
2009	0.5	111.3	36.3387	36.0
2010	0.5	113.9	42.3214	47.45
2011	0.5	113.3	32.3438	12.85
2012	0.9	118.1	38.7476	36.2
2013	3.4	98.1	36.8167	40.15

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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. 15 column(s) with >80% missing values were removed: 1985, 1986, 1987, 1988, 1989, 1990.... 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 United Nations Economic Commission for Africa and has not been independently validated by ESA.
• Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
• The following columns have >20% missing values and should be treated with caution in modelling: 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006....
• Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

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Citation

@dataset{hdx_africa_cameroon_uneca_education,
  title     = {CAMEROON - Education indicators, UNECA},
  author    = {United Nations Economic Commission for Africa},
  year      = {2024},
  url       = {https://data.humdata.org/dataset/cameroon-uneca-education},
  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.