Add README.md

README.md

@@ -1,32 +1,149 @@
 ---
+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
+- dji
+pretty_name: "Djibouti - Education Indicators"
 dataset_info:
-  features:
-  - name: indicator_id
-    dtype: string
-  - name: country_id
-    dtype: string
-  - name: year
-    dtype: int64
-  - name: value
-    dtype: float64
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 105466
-    num_examples: 1736
-  - name: test
-    num_bytes: 26295
-    num_examples: 434
-  download_size: 30692
-  dataset_size: 131761
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 1736
+    - name: test
+      num_examples: 434
 ---
+
+# Djibouti - Education Indicators
+
+**Publisher:** UNESCO · **Source:** [HDX](https://data.humdata.org/dataset/unesco-data-for-djibouti) · **License:** `cc-by-igo` · **Updated:** 2026-03-02
+
+---
+
+## Abstract
+
+Education indicators for Djibouti.
+
+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 2026-03-02. Geographic scope: **DJI**.
+
+*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)** | 2,170 |
+| **Columns** | 6 (2 numeric, 4 categorical, 0 datetime) |
+| **Train split** | 1,736 rows |
+| **Test split** | 434 rows |
+| **Geographic scope** | DJI |
+| **Publisher** | UNESCO |
+| **HDX last updated** | 2026-03-02 |
+
+---
+
+## Variables
+
+**Geographic** — `country_id` (DJI), `year` (range 1970.0–2025.0).
+
+**Outcome / Measurement** — `value` (range 0.0–2742469.0).
+
+**Identifier / Metadata** — `indicator_id` (CR.MOD.1.F, CR.MOD.2.M, 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-djibouti")
+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.M, CR.MOD.1 |
+| `country_id` | object | 0.0% | DJI |
+| `year` | int64 | 0.0% | 1970.0 – 2025.0 (mean 2010.8829) |
+| `value` | float64 | 0.0% | 0.0 – 2742469.0 (mean 11997.4449) |
+| `esa_source` | object | 0.0% | HDX |
+| `esa_processed` | object | 0.0% | 2026-04-04 |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `year` | 1970.0 | 2025.0 | 2010.8829 | 2015.0 |
+| `value` | 0.0 | 2742469.0 | 11997.4449 | 19.5058 |
+
+---
+
+## 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-djibouti) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_unesco_data_for_djibouti,
+  title     = {Djibouti - Education Indicators},
+  author    = {UNESCO},
+  year      = {2026},
+  url       = {https://data.humdata.org/dataset/unesco-data-for-djibouti},
+  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.*