| --- |
| 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](https://data.humdata.org/dataset/unesco-data-for-sierra-leone) · **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](http://data.uis.unesco.org) 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](https://huggingface.co/electricsheepafrica).* |
|
|
| --- |
|
|
| ## 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 |
|
|
| ```python |
| 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](https://data.humdata.org/dataset/unesco-data-for-sierra-leone) for the publisher's own methodology notes and caveats. |
| |
| --- |
| |
| ## Citation |
| |
| ```bibtex |
| @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](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.* |
