Add README.md

README.md

@@ -1,56 +1,162 @@
 ---
+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
+- tabular-regression
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- hxl
+- indicators
+- uzb
+pretty_name: "Uzbekistan - Historical Health Indicators"
 dataset_info:
-  features:
-  - name: gho_code
-    dtype: string
-  - name: gho_display
-    dtype: string
-  - name: year_display
-    dtype: float64
-  - name: startyear
-    dtype: float64
-  - name: endyear
-    dtype: float64
-  - name: region_code
-    dtype: string
-  - name: region_display
-    dtype: string
-  - name: country_code
-    dtype: string
-  - name: country_display
-    dtype: string
-  - name: dimension_type
-    dtype: string
-  - name: dimension_code
-    dtype: string
-  - name: dimension_name
-    dtype: string
-  - name: numeric
-    dtype: float64
-  - name: value
-    dtype: string
-  - name: low
-    dtype: float64
-  - name: high
-    dtype: float64
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 1773700
-    num_examples: 7253
-  - name: test
-    num_bytes: 447756
-    num_examples: 1814
-  download_size: 470731
-  dataset_size: 2221456
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 7253
+    - name: test
+      num_examples: 1813
 ---
+
+# Uzbekistan - Historical Health Indicators
+
+**Publisher:** World Health Organization · **Source:** [HDX](https://data.humdata.org/dataset/who-historical-data-for-uzb) · **License:** `hdx-other` · **Updated:** 2025-02-07
+
+---
+
+## Abstract
+
+This dataset contains historical data from WHO's [data portal](https://www.who.int/gho/en/).
+
+Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-02-07. Geographic scope: **UZB**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Humanitarian and development data |
+| **Unit of observation** | First-level administrative unit observations |
+| **Rows (total)** | 9,067 |
+| **Columns** | 18 (6 numeric, 12 categorical, 0 datetime) |
+| **Train split** | 7,253 rows |
+| **Test split** | 1,813 rows |
+| **Geographic scope** | UZB |
+| **Publisher** | World Health Organization |
+| **HDX last updated** | 2025-02-07 |
+
+---
+
+## Variables
+
+**Geographic** — `gho_display` (Mean BMI (kg/m&#xb2;) (crude estimate), Adolescent mortality rate (per 1 000 age specific cohort), Alcohol, recorded per capita (15+) consumption (in litres of pure alcohol)), `year_display` (range 1970.0–2025.0), `startyear` (range 1970.0–2025.0), `endyear` (range 1970.0–2025.0), `region_code` (EUR, #region+code) and 4 others.
+
+**Outcome / Measurement** — `value` (No data, No, Yes).
+
+**Identifier / Metadata** — `gho_code` (NCD_BMI_MEANC, CHILDMORT10TO19, SA_0000001400_ARCHIVED), `dimension_code` (SEX_FMLE, SEX_MLE, SEX_BTSX), `dimension_name` (Female, Male, Both sexes), `esa_source`, `esa_processed`.
+
+**Other** — `numeric` (range 0.0–1929374000.0), `low` (range 0.0–223133.0), `high` (range 0.0–674971.0).
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/asia-who-historical-data-for-uzbekistan")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
+
+print(train.shape)
+train.head()
+```
+
+---
+
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `gho_code` | object | 0.0% | NCD_BMI_MEANC, CHILDMORT10TO19, SA_0000001400_ARCHIVED |
+| `gho_display` | object | 0.0% | Mean BMI (kg/m&#xb2;) (crude estimate), Adolescent mortality rate (per 1 000 age specific cohort), Alcohol, recorded per capita (15+) consumption (in litres of pure alcohol) |
+| `year_display` | float64 | 0.0% | 1970.0 – 2025.0 (mean 2006.2031) |
+| `startyear` | float64 | 0.0% | 1970.0 – 2025.0 (mean 2006.1903) |
+| `endyear` | float64 | 0.0% | 1970.0 – 2025.0 (mean 2006.2031) |
+| `region_code` | object | 0.0% | EUR, #region+code |
+| `region_display` | object | 0.0% | Europe, #region+name |
+| `country_code` | object | 0.0% | UZB, #country+code |
+| `country_display` | object | 0.0% | Uzbekistan, #country+name |
+| `dimension_type` | object | 23.9% | SEX, WEALTHDECILE, WEALTHQUINTILE |
+| `dimension_code` | object | 23.9% | SEX_FMLE, SEX_MLE, SEX_BTSX |
+| `dimension_name` | object | 24.8% | Female, Male, Both sexes |
+| `numeric` | float64 | 31.7% | 0.0 – 1929374000.0 (mean 732255.1702) |
+| `value` | object | 0.7% | No data, No, Yes |
+| `low` | float64 | 52.1% | 0.0 – 223133.0 (mean 351.2352) |
+| `high` | float64 | 52.1% | 0.0 – 674971.0 (mean 881.657) |
+| `esa_source` | object | 0.0% |  |
+| `esa_processed` | object | 0.0% |  |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `year_display` | 1970.0 | 2025.0 | 2006.2031 | 2006.0 |
+| `startyear` | 1970.0 | 2025.0 | 2006.1903 | 2006.0 |
+| `endyear` | 1970.0 | 2025.0 | 2006.2031 | 2006.0 |
+| `numeric` | 0.0 | 1929374000.0 | 732255.1702 | 32.2 |
+| `low` | 0.0 | 223133.0 | 351.2352 | 24.7 |
+| `high` | 0.0 | 674971.0 | 881.657 | 40.2 |
+
+---
+
+## 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`. 1 column(s) with >80% missing values were removed: `gho_url`. 14 exact duplicate rows were removed. 6 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). 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 Health Organization 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: `dimension_type`, `dimension_code`, `dimension_name`, `numeric`, `low`, `high`.
+- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/who-historical-data-for-uzb) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_asia_who_historical_data_for_uzbekistan,
+  title     = {Uzbekistan - Historical Health Indicators},
+  author    = {World Health Organization},
+  year      = {2025},
+  url       = {https://data.humdata.org/dataset/who-historical-data-for-uzb},
+  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.*