Add README.md

README.md

@@ -1,24 +1,134 @@
 ---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license: other
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- tabular-classification
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- cyclones-hurricanes-typhoons
+- geodata
+- health-facilities
+- moz
+pretty_name: "Mozambique - Health facilities"
 dataset_info:
-  features:
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 1218
-    num_examples: 58
-  - name: test
-    num_bytes: 315
-    num_examples: 15
-  download_size: 2257
-  dataset_size: 1533
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 58
+    - name: test
+      num_examples: 14
 ---
+
+# Mozambique - Health facilities
+
+**Publisher:** OCHA Mozambique · **Source:** [HDX](https://data.humdata.org/dataset/mozambique-health-facilities) · **License:** `other-pd-nr` · **Updated:** 2025-04-08
+
+---
+
+## Abstract
+
+Mozambique health facilities
+
+Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-04-08. Geographic scope: **MOZ**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Public health |
+| **Unit of observation** | Tabular records |
+| **Rows (total)** | 73 |
+| **Columns** | 2 (0 numeric, 2 categorical, 0 datetime) |
+| **Train split** | 58 rows |
+| **Test split** | 14 rows |
+| **Geographic scope** | MOZ |
+| **Publisher** | OCHA Mozambique |
+| **HDX last updated** | 2025-04-08 |
+
+---
+
+## Variables
+
+**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-12).
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-mozambique-health-facilities")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
+
+print(train.shape)
+train.head()
+```
+
+---
+
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `esa_source` | object | 0.0% | HDX |
+| `esa_processed` | object | 0.0% | 2026-04-12 |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+_No numeric columns._
+
+---
+
+## 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`. 23 column(s) with >80% missing values were removed: `unnamed_0`, `unnamed_1`, `unnamed_2`, `unnamed_3`, `unnamed_4`, `unnamed_5`.... 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 OCHA Mozambique 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/mozambique-health-facilities) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_mozambique_health_facilities,
+  title     = {Mozambique - Health facilities},
+  author    = {OCHA Mozambique},
+  year      = {2025},
+  url       = {https://data.humdata.org/dataset/mozambique-health-facilities},
+  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.*