Add README.md

README.md

@@ -1,80 +1,183 @@
 ---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license: other
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- tabular-classification
+- other
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- demographics
+- health
+- ago
+pretty_name: "Angola - Subnational Demographic and Health Data"
 dataset_info:
-  features:
-  - name: iso3
-    dtype: string
-  - name: location
-    dtype: string
-  - name: dataid
-    dtype: int64
-  - name: indicator
-    dtype: string
-  - name: value
-    dtype: float64
-  - name: precision
-    dtype: int64
-  - name: dhs_countrycode
-    dtype: string
-  - name: countryname
-    dtype: string
-  - name: surveyyear
-    dtype: int64
-  - name: surveyid
-    dtype: string
-  - name: indicatorid
-    dtype: string
-  - name: indicatororder
-    dtype: int64
-  - name: indicatortype
-    dtype: string
-  - name: characteristicid
-    dtype: int64
-  - name: characteristicorder
-    dtype: int64
-  - name: characteristiccategory
-    dtype: string
-  - name: characteristiclabel
-    dtype: string
-  - name: byvariableid
-    dtype: int64
-  - name: byvariablelabel
-    dtype: string
-  - name: istotal
-    dtype: int64
-  - name: ispreferred
-    dtype: int64
-  - name: sdrid
-    dtype: string
-  - name: regionid
-    dtype: string
-  - name: surveyyearlabel
-    dtype: string
-  - name: surveytype
-    dtype: string
-  - name: denominatorweighted
-    dtype: float64
-  - name: denominatorunweighted
-    dtype: float64
-  - name: levelrank
-    dtype: int64
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 296807
-    num_examples: 875
-  - name: test
-    num_bytes: 74855
-    num_examples: 219
-  download_size: 57081
-  dataset_size: 371662
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 875
+    - name: test
+      num_examples: 218
 ---
+
+# Angola - Subnational Demographic and Health Data
+
+**Publisher:** The DHS Program · **Source:** [HDX](https://data.humdata.org/dataset/dhs-subnational-data-for-angola) · **License:** `hdx-other` · **Updated:** 2026-04-20
+
+---
+
+## Abstract
+
+Contains data from the [DHS data portal](https://api.dhsprogram.com/). There is also a dataset containing [Angola - National Demographic and Health Data](https://data.humdata.org/dataset/dhs-data-for-angola) on HDX.
+
+The DHS Program Application Programming Interface (API) provides software developers access to aggregated indicator data from The Demographic and Health Surveys (DHS) Program. The API can be used to create various applications to help analyze, visualize, explore and disseminate data on population, health, HIV, and nutrition from more than 90 countries.
+
+Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-04-20. Geographic scope: **AGO**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Public health |
+| **Unit of observation** | First-level administrative unit observations |
+| **Rows (total)** | 1,094 |
+| **Columns** | 30 (13 numeric, 17 categorical, 0 datetime) |
+| **Train split** | 875 rows |
+| **Test split** | 218 rows |
+| **Geographic scope** | AGO |
+| **Publisher** | The DHS Program |
+| **HDX last updated** | 2026-04-20 |
+
+---
+
+## Variables
+
+**Geographic** — `iso3` (AGO), `location` (Lunda Norte     , Malanje         , Lunda Sul       ), `dhs_countrycode` (AO), `countryname` (Angola), `surveyyear` (range 2006.0–2023.0) and 8 others.
+
+**Outcome / Measurement** — `value` (range 0.3–136.0), `istotal` (range 0.0–0.0).
+
+**Identifier / Metadata** — `dataid` (range 231.0–7981412.0), `indicatorid` (RH_DELP_C_DHF, CH_DIAT_C_ORT, DV_SPVL_W_POS), `characteristicid` (range 506001.0–506028.0), `characteristiclabel` (Lunda Norte     , Malanje         , Lunda Sul       ), `ispreferred` (range 0.0–1.0) and 3 others.
+
+**Other** — `indicator` (Place of delivery: Health facility, Treatment of diarrhea: Either ORS or RHF, Physical or sexual violence committed by husband/partner), `precision` (range 0.0–1.0), `indicatororder` (range 11763080.0–260321010.0), `characteristicorder` (range 1506001.0–1506028.0), `denominatorweighted` (range 6.0–5538.0) and 2 others.
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-demographics-angola")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
+
+print(train.shape)
+train.head()
+```
+
+---
+
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `iso3` | object | 0.0% | AGO |
+| `location` | object | 0.0% | Lunda Norte     , Malanje         , Lunda Sul        |
+| `dataid` | int64 | 0.0% | 231.0 – 7981412.0 (mean 4361339.8254) |
+| `indicator` | object | 0.0% | Place of delivery: Health facility, Treatment of diarrhea: Either ORS or RHF, Physical or sexual violence committed by husband/partner |
+| `value` | float64 | 0.0% | 0.3 – 136.0 (mean 26.9424) |
+| `precision` | int64 | 0.0% | 0.0 – 1.0 (mean 0.9269) |
+| `dhs_countrycode` | object | 0.0% | AO |
+| `countryname` | object | 0.0% | Angola |
+| `surveyyear` | int64 | 0.0% | 2006.0 – 2023.0 (mean 2018.4004) |
+| `surveyid` | object | 0.0% | AO2015DHS, AO2023DHS, AO2006MIS |
+| `indicatorid` | object | 0.0% | RH_DELP_C_DHF, CH_DIAT_C_ORT, DV_SPVL_W_POS |
+| `indicatororder` | int64 | 0.0% | 11763080.0 – 260321010.0 (mean 108330851.7459) |
+| `indicatortype` | object | 0.0% | I |
+| `characteristicid` | int64 | 0.0% | 506001.0 – 506028.0 (mean 506018.6188) |
+| `characteristicorder` | int64 | 0.0% | 1506001.0 – 1506028.0 (mean 1506018.6188) |
+| `characteristiccategory` | object | 0.0% | Region |
+| `characteristiclabel` | object | 0.0% | Lunda Norte     , Malanje         , Lunda Sul        |
+| `byvariableid` | int64 | 0.0% | 0.0 – 631002.0 (mean 34358.7285) |
+| `byvariablelabel` | object | 72.1% |  |
+| `istotal` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
+| `ispreferred` | int64 | 0.0% | 0.0 – 1.0 (mean 0.8793) |
+| `sdrid` | object | 0.0% |  |
+| `regionid` | object | 0.0% |  |
+| `surveyyearlabel` | object | 0.0% |  |
+| `surveytype` | object | 0.0% |  |
+| `denominatorweighted` | float64 | 21.2% | 6.0 – 5538.0 (mean 474.8921) |
+| `denominatorunweighted` | float64 | 21.2% | 27.0 – 2479.0 (mean 479.4954) |
+| `levelrank` | int64 | 0.0% | 1.0 – 1.0 (mean 1.0) |
+| `esa_source` | object | 0.0% |  |
+| `esa_processed` | object | 0.0% |  |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `dataid` | 231.0 | 7981412.0 | 4361339.8254 | 4277082.0 |
+| `value` | 0.3 | 136.0 | 26.9424 | 23.25 |
+| `precision` | 0.0 | 1.0 | 0.9269 | 1.0 |
+| `surveyyear` | 2006.0 | 2023.0 | 2018.4004 | 2015.0 |
+| `indicatororder` | 11763080.0 | 260321010.0 | 108330851.7459 | 99166030.0 |
+| `characteristicid` | 506001.0 | 506028.0 | 506018.6188 | 506019.0 |
+| `characteristicorder` | 1506001.0 | 1506028.0 | 1506018.6188 | 1506019.0 |
+| `byvariableid` | 0.0 | 631002.0 | 34358.7285 | 0.0 |
+| `istotal` | 0.0 | 0.0 | 0.0 | 0.0 |
+| `ispreferred` | 0.0 | 1.0 | 0.8793 | 1.0 |
+| `denominatorweighted` | 6.0 | 5538.0 | 474.8921 | 249.5 |
+| `denominatorunweighted` | 27.0 | 2479.0 | 479.4954 | 397.5 |
+| `levelrank` | 1.0 | 1.0 | 1.0 | 1.0 |
+
+---
+
+## 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`. 2 column(s) with >80% missing values were removed: `cilow`, `cihigh`. 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 The DHS Program 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: `byvariablelabel`, `denominatorweighted`, `denominatorunweighted`.
+- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/dhs-subnational-data-for-angola) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_demographics_angola,
+  title     = {Angola - Subnational Demographic and Health Data},
+  author    = {The DHS Program},
+  year      = {2026},
+  url       = {https://data.humdata.org/dataset/dhs-subnational-data-for-angola},
+  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.*