annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- demographics
- education
- indicators
- socioeconomics
- sustainable-development
- sustainable-development-goals-sdg
- sle
pretty_name: Sierra Leone - Education Indicators
dataset_info:
splits:
- name: train
num_examples: 5315
- name: test
num_examples: 1328
Sierra Leone - Education Indicators
Publisher: UNESCO · Source: HDX · License: cc-by-igo · Updated: 2026-03-03
Abstract
Education indicators for Sierra Leone.
Contains data from the UNESCO Institute for Statistics bulk data service covering the following categories: SDG 4 Global and Thematic (made 2026 February), Other Policy Relevant Indicators (made 2026 February), Demographic and Socio-economic (made 2026 February)
Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-03. Geographic scope: SLE.
Curated into ML-ready Parquet format by Electric Sheep Africa.
Dataset Characteristics
| Domain | Education |
| Unit of observation | Country-level aggregates |
| Rows (total) | 6,644 |
| Columns | 6 (2 numeric, 4 categorical, 0 datetime) |
| Train split | 5,315 rows |
| Test split | 1,328 rows |
| Geographic scope | SLE |
| Publisher | UNESCO |
| HDX last updated | 2026-03-03 |
Variables
Geographic — country_id (SLE), year (range 1971.0–2025.0).
Outcome / Measurement — value (range 0.0–4608987.0).
Identifier / Metadata — indicator_id (CR.MOD.1.F, CR.MOD.2.GPIA, CR.MOD.1), esa_source (HDX), esa_processed (2026-04-04).
Quick Start
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-unesco-data-for-sierra-leone")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()
print(train.shape)
train.head()
Schema
| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
indicator_id |
object | 0.0% | CR.MOD.1.F, CR.MOD.2.GPIA, CR.MOD.1 |
country_id |
object | 0.0% | SLE |
year |
int64 | 0.0% | 1971.0 – 2025.0 (mean 2013.0214) |
value |
float64 | 0.0% | 0.0 – 4608987.0 (mean 3920.8324) |
esa_source |
object | 0.0% | HDX |
esa_processed |
object | 0.0% | 2026-04-04 |
Numeric Summary
| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
year |
1971.0 | 2025.0 | 2013.0214 | 2015.0 |
value |
0.0 | 4608987.0 | 3920.8324 | 13.1968 |
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. 2 column(s) with >80% missing values were removed: magnitude, qualifier. The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.
Limitations
- Data originates from UNESCO 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.
Citation
@dataset{hdx_africa_unesco_data_for_sierra_leone,
title = {Sierra Leone - Education Indicators},
author = {UNESCO},
year = {2026},
url = {https://data.humdata.org/dataset/unesco-data-for-sierra-leone},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.