Add README.md

README.md

@@ -1,36 +1,147 @@
 ---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license: cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- tabular-classification
+- tabular-regression
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- development
+- indicators
+- moz
+pretty_name: "Mozambique - Urban Development"
 dataset_info:
-  features:
-  - name: country_name
-    dtype: string
-  - name: country_iso3
-    dtype: string
-  - name: year
-    dtype: int64
-  - name: indicator_name
-    dtype: string
-  - name: indicator_code
-    dtype: string
-  - name: value
-    dtype: float64
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 65515
-    num_examples: 513
-  - name: test
-    num_bytes: 16676
-    num_examples: 129
-  download_size: 16241
-  dataset_size: 82191
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 513
+    - name: test
+      num_examples: 128
 ---
+
+# Mozambique - Urban Development
+
+**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-urban-development-indicators-for-mozambique) · **License:** `cc-by` · **Updated:** 2026-03-27
+
+---
+
+## Abstract
+
+Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-mozambique) on HDX.
+
+Cities can be tremendously efficient. It is easier to provide water and sanitation to people living closer together, while access to health, education, and other social and cultural services is also much more readily available. However, as cities grow, the cost of meeting basic needs increases, as does the strain on the environment and natural resources. Data on urbanization, traffic and congestion, and air pollution are from the United Nations Population Division, World Health Organization, International Road Federation, World Resources Institute, and other sources.
+
+Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. 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** | Country-level aggregates |
+| **Rows (total)** | 642 |
+| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
+| **Train split** | 513 rows |
+| **Test split** | 128 rows |
+| **Geographic scope** | MOZ |
+| **Publisher** | World Bank Group |
+| **HDX last updated** | 2026-03-27 |
+
+---
+
+## Variables
+
+**Geographic** — `country_name` (Mozambique), `country_iso3` (MOZ), `year` (range 1960.0–2025.0).
+
+**Outcome / Measurement** — `value` (range 0.0103–12617472.0).
+
+**Identifier / Metadata** — `indicator_name` (Population in the largest city (% of urban population), Population in largest city, Population in urban agglomerations of more than 1 million), `indicator_code` (EN.URB.LCTY.UR.ZS, EN.URB.LCTY, EN.URB.MCTY), `esa_source` (HDX), `esa_processed` (2026-04-12).
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-world-bank-urban-development-indicators-for-mozambique")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
+
+print(train.shape)
+train.head()
+```
+
+---
+
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `country_name` | object | 0.0% | Mozambique |
+| `country_iso3` | object | 0.0% | MOZ |
+| `year` | int64 | 0.0% | 1960.0 – 2025.0 (mean 1995.1978) |
+| `indicator_name` | object | 0.0% | Population in the largest city (% of urban population), Population in largest city, Population in urban agglomerations of more than 1 million |
+| `indicator_code` | object | 0.0% | EN.URB.LCTY.UR.ZS, EN.URB.LCTY, EN.URB.MCTY |
+| `value` | float64 | 0.0% | 0.0103 – 12617472.0 (mean 662059.4702) |
+| `esa_source` | object | 0.0% | HDX |
+| `esa_processed` | object | 0.0% | 2026-04-12 |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `year` | 1960.0 | 2025.0 | 1995.1978 | 1998.0 |
+| `value` | 0.0103 | 12617472.0 | 662059.4702 | 30.137 |
+
+---
+
+## 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`. 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 World Bank Group 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/world-bank-urban-development-indicators-for-mozambique) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_world_bank_urban_development_indicators_for_mozambique,
+  title     = {Mozambique - Urban Development},
+  author    = {World Bank Group},
+  year      = {2026},
+  url       = {https://data.humdata.org/dataset/world-bank-urban-development-indicators-for-mozambique},
+  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.*