Add README.md

README.md

@@ -1,68 +1,165 @@
 ---
+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
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- eastern-africa
+- humanitarian-needs-overview-hno
+- dji
+- eth
+- ken
+- ssd
+- sdn
+pretty_name: "Eastern Africa Region People in Need Per Sector 2011-2015"
 dataset_info:
-  features:
-  - name: year
-    dtype: float64
-  - name: country
-    dtype: string
-  - name: pin
-    dtype: string
-  - name: fs_pin
-    dtype: string
-  - name: fs_tar
-    dtype: string
-  - name: nut_pin
-    dtype: string
-  - name: nut_tar
-    dtype: string
-  - name: health_pin
-    dtype: string
-  - name: healthtar
-    dtype: string
-  - name: wash_pin
-    dtype: string
-  - name: wash_tar
-    dtype: string
-  - name: edu_pin
-    dtype: string
-  - name: edu_tar
-    dtype: string
-  - name: shelter_nfi_pin
-    dtype: string
-  - name: shelter_nfi_tar
-    dtype: string
-  - name: protection_pin
-    dtype: string
-  - name: protection_tar
-    dtype: string
-  - name: multi_sector_pin
-    dtype: string
-  - name: multi_sector_tar
-    dtype: string
-  - name: mine_action_pin
-    dtype: string
-  - name: mine_action_tar
-    dtype: string
-  - name: source
-    dtype: string
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 4432
-    num_examples: 20
-  - name: test
-    num_bytes: 1115
-    num_examples: 5
-  download_size: 21990
-  dataset_size: 5547
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 20
+    - name: test
+      num_examples: 5
 ---
+
+# Eastern Africa Region People in Need Per Sector 2011-2015
+
+**Publisher:** OCHA Regional Office for Southern and Eastern Africa (ROSEA) · **Source:** [HDX](https://data.humdata.org/dataset/eastern-africa-region-people-in-need-per-sector-2011-2014) · **License:** `cc-by-igo` · **Updated:** 2023-09-28
+
+---
+
+## Abstract
+
+Data on people in need per sector in Kenya, Somalia, Sudan, South Sudan and Ethiopia from 2011 to 2015
+
+Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2023-09-28. Geographic scope: **DJI, ETH, KEN, SSD, SDN**.
+
+*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)** | 25 |
+| **Columns** | 24 (1 numeric, 23 categorical, 0 datetime) |
+| **Train split** | 20 rows |
+| **Test split** | 5 rows |
+| **Geographic scope** | DJI, ETH, KEN, SSD, SDN |
+| **Publisher** | OCHA Regional Office for Southern and Eastern Africa (ROSEA) |
+| **HDX last updated** | 2023-09-28 |
+
+---
+
+## Variables
+
+**Geographic** — `year` (range 2011.0–2014.0), `country` (SOM, SUD, SSD).
+
+**Identifier / Metadata** — `source`, `esa_source`, `esa_processed`.
+
+**Other** — `pin` (2,100,000, 1,700,000, 3,800,000), `fs_pin` (3,750,000, 3,200,000, 257,000), `fs_tar` (2,200,000, 3,750,000, 1,981,000), `nut_pin` (3,900,000, 172,500, 2,999,937), `nut_tar` (107,000, 475,000, 591,000) and 14 others.
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-eastern-africa-region-people-in-need-per-sector-2011-2014")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
+
+print(train.shape)
+train.head()
+```
+
+---
+
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `year` | float64 | 4.0% | 2011.0 – 2014.0 (mean 2012.5) |
+| `country` | object | 4.0% | SOM, SUD, SSD |
+| `pin` | object | 68.0% | 2,100,000, 1,700,000, 3,800,000 |
+| `fs_pin` | object | 20.0% | 3,750,000, 3,200,000, 257,000 |
+| `fs_tar` | object | 24.0% | 2,200,000, 3,750,000, 1,981,000 |
+| `nut_pin` | object | 8.0% | 3,900,000, 172,500, 2,999,937 |
+| `nut_tar` | object | 28.0% | 107,000, 475,000, 591,000 |
+| `health_pin` | object | 20.0% | 222,500, 7,500,000, 7,770,000 |
+| `healthtar` | object | 24.0% | 164,800, 3,700,000, 3,549,955 |
+| `wash_pin` | object | 20.0% | 3,751,000, 2,000,000, 300,000 |
+| `wash_tar` | object | 28.0% | 2,600,000, 2,549,000, 2,500,000 |
+| `edu_pin` | object | 36.0% |  |
+| `edu_tar` | object | 40.0% |  |
+| `shelter_nfi_pin` | object | 52.0% |  |
+| `shelter_nfi_tar` | object | 52.0% |  |
+| `protection_pin` | object | 60.0% |  |
+| `protection_tar` | object | 56.0% |  |
+| `multi_sector_pin` | object | 56.0% |  |
+| `multi_sector_tar` | object | 56.0% |  |
+| `mine_action_pin` | object | 72.0% |  |
+| `mine_action_tar` | object | 72.0% |  |
+| `source` | object | 16.0% |  |
+| `esa_source` | object | 0.0% |  |
+| `esa_processed` | object | 0.0% |  |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `year` | 2011.0 | 2014.0 | 2012.5 | 2012.5 |
+
+---
+
+## 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`. 20 column(s) with >80% missing values were removed: `tar`, `rch`, `fs_rch`, `nut_rch`, `healthrch`, `wash_rch`.... 2 exact duplicate rows were removed. 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 Regional Office for Southern and Eastern Africa (ROSEA) and has not been independently validated by ESA.
+- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
+- The following columns have >20% missing values and should be treated with caution in modelling: `pin`, `fs_tar`, `nut_tar`, `healthtar`, `wash_tar`, `edu_pin`, `edu_tar`, `shelter_nfi_pin`....
+- This dataset spans 5 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
+- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/eastern-africa-region-people-in-need-per-sector-2011-2014) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_eastern_africa_region_people_in_need_per_sector_2011_2014,
+  title     = {Eastern Africa Region People in Need Per Sector 2011-2015},
+  author    = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)},
+  year      = {2023},
+  url       = {https://data.humdata.org/dataset/eastern-africa-region-people-in-need-per-sector-2011-2014},
+  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.*