Add README.md

README.md

@@ -1,48 +1,160 @@
 ---
+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
+- asylum-seekers
+- internally-displaced-persons-idp
+- population
+- refugees
+- stateless-persons
+- mus
+pretty_name: "Mauritius - Data on forcibly displaced populations and stateless persons"
 dataset_info:
-  features:
-  - name: year
-    dtype: int64
-  - name: country_of_origin_code
-    dtype: string
-  - name: country_of_asylum_code
-    dtype: string
-  - name: country_of_origin_name
-    dtype: string
-  - name: country_of_asylum_name
-    dtype: string
-  - name: refugees
-    dtype: int64
-  - name: asylum_seekers
-    dtype: int64
-  - name: other_people_in_need_of_international_protection
-    dtype: int64
-  - name: internally_displaced_persons
-    dtype: int64
-  - name: stateless_persons
-    dtype: int64
-  - name: others_of_concern_to_unhcr
-    dtype: int64
-  - name: host_community
-    dtype: int64
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 19686
-    num_examples: 149
-  - name: test
-    num_bytes: 4801
-    num_examples: 38
-  download_size: 15576
-  dataset_size: 24487
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 149
+    - name: test
+      num_examples: 37
 ---
+
+# Mauritius - Data on forcibly displaced populations and stateless persons
+
+**Publisher:** UNHCR - The UN Refugee Agency · **Source:** [HDX](https://data.humdata.org/dataset/unhcr-population-data-for-mus) · **License:** `cc-by-igo` · **Updated:** 2026-02-25
+
+---
+
+## Abstract
+
+Data collated by UNHCR, containing information about forcibly displaced populations and stateless persons, spanning across more than 70 years of statistical activities.  The data includes the countries / territories of asylum and origin.  Specific resources are available for end-year population totals, demographics, asylum applications, decisions, and solutions availed by refugees and IDPs (resettlement, naturalisation or returns).
+
+Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-02-25. Geographic scope: **MUS**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Demographics and population |
+| **Unit of observation** | First-level administrative unit observations |
+| **Rows (total)** | 187 |
+| **Columns** | 14 (8 numeric, 6 categorical, 0 datetime) |
+| **Train split** | 149 rows |
+| **Test split** | 37 rows |
+| **Geographic scope** | MUS |
+| **Publisher** | UNHCR - The UN Refugee Agency |
+| **HDX last updated** | 2026-02-25 |
+
+---
+
+## Variables
+
+**Geographic** — `year` (range 1995.0–2025.0), `country_of_origin_code` (MUS), `country_of_asylum_code` (USA, CAN, FRA), `country_of_origin_name` (Mauritius), `country_of_asylum_name` (United States of America, Canada, France) and 4 others.
+
+**Identifier / Metadata** — `refugees` (range 0.0–99.0), `esa_source` (HDX), `esa_processed` (2026-04-04).
+
+**Other** — `other_people_in_need_of_international_protection` (range 0.0–0.0), `others_of_concern_to_unhcr` (range 0.0–0.0).
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-unhcr-population-data-for-mus")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
+
+print(train.shape)
+train.head()
+```
+
+---
+
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `year` | int64 | 0.0% | 1995.0 – 2025.0 (mean 2014.0) |
+| `country_of_origin_code` | object | 0.0% | MUS |
+| `country_of_asylum_code` | object | 0.0% | USA, CAN, FRA |
+| `country_of_origin_name` | object | 0.0% | Mauritius |
+| `country_of_asylum_name` | object | 0.0% | United States of America, Canada, France |
+| `refugees` | int64 | 0.0% | 0.0 – 99.0 (mean 14.5829) |
+| `asylum_seekers` | int64 | 0.0% | 0.0 – 235.0 (mean 25.7487) |
+| `other_people_in_need_of_international_protection` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
+| `internally_displaced_persons` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
+| `stateless_persons` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
+| `others_of_concern_to_unhcr` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
+| `host_community` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
+| `esa_source` | object | 0.0% | HDX |
+| `esa_processed` | object | 0.0% | 2026-04-04 |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `year` | 1995.0 | 2025.0 | 2014.0 | 2015.0 |
+| `refugees` | 0.0 | 99.0 | 14.5829 | 7.0 |
+| `asylum_seekers` | 0.0 | 235.0 | 25.7487 | 6.0 |
+| `other_people_in_need_of_international_protection` | 0.0 | 0.0 | 0.0 | 0.0 |
+| `internally_displaced_persons` | 0.0 | 0.0 | 0.0 | 0.0 |
+| `stateless_persons` | 0.0 | 0.0 | 0.0 | 0.0 |
+| `others_of_concern_to_unhcr` | 0.0 | 0.0 | 0.0 | 0.0 |
+| `host_community` | 0.0 | 0.0 | 0.0 | 0.0 |
+
+---
+
+## 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 UNHCR - The UN Refugee Agency 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/unhcr-population-data-for-mus) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_unhcr_population_data_for_mus,
+  title     = {Mauritius - Data on forcibly displaced populations and stateless persons},
+  author    = {UNHCR - The UN Refugee Agency},
+  year      = {2026},
+  url       = {https://data.humdata.org/dataset/unhcr-population-data-for-mus},
+  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.*