Add README.md

README.md

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 ---
+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-regression
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- gender-and-age-disaggregated-data-gadd
+- humanitarian-needs-overview-hno
+- hxl
+- people-in-need-pin
+- cmr
+pretty_name: "Cameroon: Humanitarian Needs"
 dataset_info:
-  features:
-  - name: description
-    dtype: string
-  - name: cluster
-    dtype: string
-  - name: population
-    dtype: int64
-  - name: in_need
-    dtype: int64
-  - name: targeted
-    dtype: int64
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 69
-    num_examples: 1
-  download_size: 3128
-  dataset_size: 69
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+    - name: train
+      num_examples: 0
+    - name: test
+      num_examples: 0
 ---
+
+# Cameroon: Humanitarian Needs
+
+**Publisher:** OCHA Humanitarian Programme Cycle Tools (HPC Tools) · **Source:** [HDX](https://data.humdata.org/dataset/cameroon-humanitarian-needs) · **License:** `cc-by` · **Updated:** 2026-02-13
+
+---
+
+## Abstract
+
+This dataset was compiled by the United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA) on behalf of the Humanitarian Country Team and partners. It provides the Humanitarian Country Team’s shared understanding of the crisis, including the most pressing humanitarian need and the estimated number of people who need assistance, and represents a consolidated evidence base and helps inform joint strategic response planning.
+
+Each row in this dataset represents geolocated point observations. Data was last updated on HDX on 2026-02-13. Geographic scope: **CMR**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Demographics and population |
+| **Unit of observation** | Geolocated point observations |
+| **Rows (total)** | 1 |
+| **Columns** | 7 (3 numeric, 4 categorical, 0 datetime) |
+| **Train split** | 0 rows |
+| **Test split** | 0 rows |
+| **Geographic scope** | CMR |
+| **Publisher** | OCHA Humanitarian Programme Cycle Tools (HPC Tools) |
+| **HDX last updated** | 2026-02-13 |
+
+---
+
+## Variables
+
+**Geographic** — `population` (range 29900000.0–29900000.0).
+
+**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-04).
+
+**Other** — `description` (GHO Estimates), `cluster` (ALL), `in_need` (range 2900000.0–2900000.0), `targeted` (range 1800000.0–1800000.0).
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-cameroon-humanitarian-needs")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
+
+print(train.shape)
+train.head()
+```
+
+---
+
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `description` | object | 0.0% | GHO Estimates |
+| `cluster` | object | 0.0% | ALL |
+| `population` | int64 | 0.0% | 29900000.0 – 29900000.0 (mean 29900000.0) |
+| `in_need` | int64 | 0.0% | 2900000.0 – 2900000.0 (mean 2900000.0) |
+| `targeted` | int64 | 0.0% | 1800000.0 – 1800000.0 (mean 1800000.0) |
+| `esa_source` | object | 0.0% | HDX |
+| `esa_processed` | object | 0.0% | 2026-04-04 |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `population` | 29900000.0 | 29900000.0 | 29900000.0 | 29900000.0 |
+| `in_need` | 2900000.0 | 2900000.0 | 2900000.0 | 2900000.0 |
+| `targeted` | 1800000.0 | 1800000.0 | 1800000.0 | 1800000.0 |
+
+---
+
+## 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`. 4 column(s) with >80% missing values were removed: `category`, `affected`, `reached`, `info`. 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 Humanitarian Programme Cycle Tools (HPC Tools) 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/cameroon-humanitarian-needs) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_cameroon_humanitarian_needs,
+  title     = {Cameroon: Humanitarian Needs},
+  author    = {OCHA Humanitarian Programme Cycle Tools (HPC Tools)},
+  year      = {2026},
+  url       = {https://data.humdata.org/dataset/cameroon-humanitarian-needs},
+  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.*