Add README.md

README.md

@@ -1,103 +1,147 @@
 ---
-license: gpl
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license: cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- tabular-classification
+- tabular-regression
+task_ids: []
 tags:
-- biology
-- public
-- health
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
 - gender
-- women
-- contraception
-- pregnancy
-size_categories:
-- 10K<n<100K
+- indicators
+- mar
+pretty_name: "Morocco - Gender"
 dataset_info:
-  features:
-  - name: country_name
-    dtype: string
-  - name: country_iso3
-    dtype: string
-  - name: year
-    dtype: int64
-  - name: indicator_name
-    dtype: string
-  - name: indicator_code
-    dtype: string
-  - name: value
-    dtype: float64
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 559211
-    num_examples: 4200
-  - name: test
-    num_bytes: 140070
-    num_examples: 1051
-  download_size: 104277
-  dataset_size: 699281
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 4200
+    - name: test
+      num_examples: 1050
 ---
-# Africa Unmet Need for Contraception Dataset
 
-## Dataset Summary
-Annual share of women with unmet need for contraception among married women ages 15–49 for African countries. Data are provided as percentages. Cleaned and reformatted for ML pipelines with Africa-only subsets in long and pivot formats.
+# Morocco - Gender
+
+**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-gender-indicators-for-morocco) · **License:** `cc-by` · **Updated:** 2026-03-27
+
+---
+
+## Abstract
+
+Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-morocco) on HDX.
 
-- Indicator: World Bank — Unmet need for contraception (% of married women ages 15–49) (`SP.UWT.TFRT`)
-- Geographic scope: 54 African countries (ISO‑3 list consistent across this repo)
-- Temporal coverage in files: 1960–2024 (years present; observations begin much later for most countries)
+Gender equality is a core development objective in its own right. It is also smart development policy and sound business practice. It is integral to economic growth, business growth and good development outcomes. Gender equality can boost productivity, enhance prospects for the next generation, build resilience, and make institutions more representative and effective.  In December 2015, the World Bank Group Board discussed our new Gender Equality Strategy 2016-2023, which aims to address persistent gaps and proposed a sharpened focus on more and better gender data. The Bank Group is continually scaling up commitments and expanding partnerships to fill significant gaps in gender data. The database hosts the latest sex-disaggregated data and gender statistics covering demography, education, health, access to economic opportunities, public life and decision-making, and agency.
 
-## Source & Licensing
-- Original data: https://data.worldbank.org/indicator/SP.UWT.TFRT  
-  Accessed 2025-08-10
-- License: Creative Commons Attribution 4.0 International (CC-BY-4.0)
+Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-27. Geographic scope: **MAR**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
 
 ---
 
-## Intended Uses
-- Feature in demographic, public health, and social policy models
-- Trend analysis of family planning and reproductive health indicators
-- Input to cross-country forecasting or clustering (with caution about missingness)
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Public health |
+| **Unit of observation** | Country-level aggregates |
+| **Rows (total)** | 5,251 |
+| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
+| **Train split** | 4,200 rows |
+| **Test split** | 1,050 rows |
+| **Geographic scope** | MAR |
+| **Publisher** | World Bank Group |
+| **HDX last updated** | 2026-03-27 |
+
+---
+
+## Variables
+
+**Geographic** — `country_name` (Morocco), `country_iso3` (MAR), `year` (range 1960.0–2025.0).
+
+**Outcome / Measurement** — `value` (range 0.0–3034679.0).
 
-Not suited for sub‑national analyses; indicator is national aggregate.
+**Identifier / Metadata** — `indicator_name` (Age population, age 00, female, Age population, age 00, male, Age population, age 02, female), `indicator_code` (SP.POP.AG00.FE.IN, SP.POP.AG00.MA.IN, SP.POP.AG02.FE.IN), `esa_source` (HDX), `esa_processed` (2026-04-27).
 
 ---
 
-## Processing Summary
+## Quick Start
 
-We filled missing values using linear interpolation over time, followed by forward fill and backwards fill. Early years prior to a country’s first observed year are imputed via backwards fill; only countries with no observations remain all NaN.
+```python
+from datasets import load_dataset
 
+ds    = load_dataset("electricsheepafrica/africa-gender-morocco")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
 
-## Coverage & Data Quality Notes
-- Overall non‑null share in long dataset: ~8.23% (289 of 3,510)
-- Earliest non‑missing observation overall: 1984
-- Countries with earliest original observation ≥ 2015: Angola, Somalia
-- Countries with no observations (entirely NaN across all years): Djibouti, Seychelles
-- Earlier years are imputed via backward fill for any country with at least one observation; treat imputed values with caution.
+print(train.shape)
+train.head()
+```
 
 ---
 
-## Limitations & Caveats
-- Sparse historical coverage; many countries begin in the mid‑1980s or later, the latest first‑year is 2019.
-- Interpolation + forward fill + backward fill impute continuity across time; early years before the first observation are back‑filled. Consider sensitivity analyses excluding imputed years.
-- Indicator represents national aggregates; methodological updates by the World Bank may affect cross‑country comparability.
-- Use with caution in causal inference; consider triangulating with survey microdata where available.
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `country_name` | object | 0.0% | Morocco |
+| `country_iso3` | object | 0.0% | MAR |
+| `year` | int64 | 0.0% | 1960.0 – 2025.0 (mean 1999.1811) |
+| `indicator_name` | object | 0.0% | Age population, age 00, female, Age population, age 00, male, Age population, age 02, female |
+| `indicator_code` | object | 0.0% | SP.POP.AG00.FE.IN, SP.POP.AG00.MA.IN, SP.POP.AG02.FE.IN |
+| `value` | float64 | 0.0% | 0.0 – 3034679.0 (mean 71290.1719) |
+| `esa_source` | object | 0.0% | HDX |
+| `esa_processed` | object | 0.0% | 2026-04-27 |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `year` | 1960.0 | 2025.0 | 1999.1811 | 2001.0 |
+| `value` | 0.0 | 3034679.0 | 71290.1719 | 47.0287 |
+
+---
+
+## 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 World Bank Group 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/world-bank-gender-indicators-for-morocco) for the publisher's own methodology notes and caveats.
 
 ---
 
 ## Citation
-> World Bank. *Unmet need for contraception (% of married women ages 15–49)* (`SP.UWT.TFRT`).
 
-> Electric Sheep (2025). *Africa Unmet Need for Contraception ML Dataset*.
+```bibtex
+@dataset{hdx_africa_gender_morocco,
+  title     = {Morocco - Gender},
+  author    = {World Bank Group},
+  year      = {2026},
+  url       = {https://data.humdata.org/dataset/world-bank-gender-indicators-for-morocco},
+  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
+}
+```
 
 ---
 
-## Contact
-Questions or issues: `kossisoroyce@gmail.com`.
+*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*