Datasets:

electricsheepafrica
/

africa-prevalence-de-la-malnutrition-en-rdc

@@ -1,170 +1,144 @@
 ---
-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
 - tabular-regression
-task_ids: []
 tags:
-- africa
-- humanitarian
-- hdx
-- electric-sheep-africa
-- global-acute-malnutrition-gam
-- hxl
-- malnutrition
-- nutrition
-- severe-acute-malnutrition-sam
-- cod
-pretty_name: "Démocratique République du Congo : Prévalence de la malnutrition"
-dataset_info:
-  splits:
-    - name: train
-      num_examples: 416
-    - name: test
-      num_examples: 104
 ---
-# Démocratique République du Congo : Prévalence de la malnutrition
-**Publisher:** UNICEF Democratic Republic of Congo · **Source:** [HDX](https://data.humdata.org/dataset/prevalence-de-la-malnutrition-en-rdc) · **License:** `cc-by-igo` · **Updated:** 2025-05-05
----
-## Abstract
-Prévalence de la malnutrition pour les enfants et les Femmes enceintes et allaitantes désagrégés par zone de santé. Les données sont à jours
-Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-05-05. Geographic scope: **COD**.
-*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
----
-## Dataset Characteristics
-| | |
 |---|---|
-| **Domain** | Food security and nutrition |
-| **Unit of observation** | First-level administrative unit observations |
-| **Rows (total)** | 521 |
-| **Columns** | 20 (12 numeric, 8 categorical, 0 datetime) |
-| **Train split** | 416 rows |
-| **Test split** | 104 rows |
-| **Geographic scope** | COD |
-| **Publisher** | UNICEF Democratic Republic of Congo |
-| **HDX last updated** | 2025-05-05 |
----
-## Variables
-**Geographic** — `province` (Ituri, Kinshasa, Sud-Kivu), `code_province` (CD54, CD10, CD61), `zone_de_sante` (Bili, Lubunga, Ferekeni).
-**Identifier / Metadata** — `code_terrtoire` (CD1000, CD5405, CD7101), `pcode_zs` (CD1000ZS01, CD6208ZS01, CD6313ZS02), `esa_source` (HDX), `esa_processed` (2026-04-17).
-**Other** — `territoire` (Kinshasa, Djugu, Lubumbashi), `prévalence_mam` (range 0.5–20.1), `prévalence_mas` (range 0.0–8.4), `prévalence_mag` (range 1.1–26.6), `sévérité_mas` (range 1.0–1213.0) and 8 others.
----
-## Quick Start
 ```python
 from datasets import load_dataset
-ds    = load_dataset("electricsheepafrica/africa-prevalence-de-la-malnutrition-en-rdc")
-train = ds["train"].to_pandas()
-test  = ds["test"].to_pandas()
-print(train.shape)
-train.head()
 ```
----
-## Schema
-| Column | Type | Null % | Range / Sample Values |
-|---|---|---|---|
-| `province` | object | 0.2% | Ituri, Kinshasa, Sud-Kivu |
-| `code_province` | object | 0.4% | CD54, CD10, CD61 |
-| `territoire` | object | 0.2% | Kinshasa, Djugu, Lubumbashi |
-| `code_terrtoire` | object | 0.4% | CD1000, CD5405, CD7101 |
-| `zone_de_sante` | object | 0.2% | Bili, Lubunga, Ferekeni |
-| `pcode_zs` | object | 0.4% | CD1000ZS01, CD6208ZS01, CD6313ZS02 |
-| `prévalence_mam` | float64 | 0.4% | 0.5 – 20.1 (mean 6.006) |
-| `prévalence_mas` | float64 | 0.4% | 0.0 – 8.4 (mean 2.3678) |
-| `prévalence_mag` | float64 | 0.4% | 1.1 – 26.6 (mean 8.3748) |
-| `sévérité_mas` | float64 | 0.2% | 1.0 – 1213.0 (mean 4.6654) |
-| `sévérité_mag` | float64 | 0.2% | 1.0 – 1070.0 (mean 4.1154) |
-| `sévérité_ipc_nut` | float64 | 0.2% | 0.0 – 0.0 (mean 0.0) |
-| `ipc_actuelle` | float64 | 0.2% | 0.0 – 38.0 (mean 0.1462) |
-| `ipc_projeté` | float64 | 0.4% | 0.0 – 4.0 (mean 2.341) |
-| `sévérité_impact_malnutrition` | float64 | 0.2% | 1.0 – 1260.0 (mean 4.8462) |
-| `ej_impact_malnutrition` | float64 | 0.2% | 0.0 – 250.0 (mean 0.9615) |
-| `sévérité_impact_malnutrition_ej` | float64 | 0.2% | 1.0 – 1353.0 (mean 5.2038) |
-| `sévérité_impact_malnutrition_3_4_5` | float64 | 0.2% | 0.0 – 754.0 (mean 2.9) |
-| `esa_source` | object | 0.0% | HDX |
-| `esa_processed` | object | 0.0% | 2026-04-17 |
----
-## Numeric Summary
-| Column | Min | Max | Mean | Median |
-|---|---|---|---|---|
-| `prévalence_mam` | 0.5 | 20.1 | 6.006 | 5.7 |
-| `prévalence_mas` | 0.0 | 8.4 | 2.3678 | 1.9 |
-| `prévalence_mag` | 1.1 | 26.6 | 8.3748 | 7.9 |
-| `sévérité_mas` | 1.0 | 1213.0 | 4.6654 | 2.0 |
-| `sévérité_mag` | 1.0 | 1070.0 | 4.1154 | 2.0 |
-| `sévérité_ipc_nut` | 0.0 | 0.0 | 0.0 | 0.0 |
-| `ipc_actuelle` | 0.0 | 38.0 | 0.1462 | 0.0 |
-| `ipc_projeté` | 0.0 | 4.0 | 2.341 | 3.0 |
-| `sévérité_impact_malnutrition` | 1.0 | 1260.0 | 4.8462 | 2.0 |
-| `ej_impact_malnutrition` | 0.0 | 250.0 | 0.9615 | 0.0 |
-| `sévérité_impact_malnutrition_ej` | 1.0 | 1353.0 | 5.2038 | 3.0 |
-| `sévérité_impact_malnutrition_3_4_5` | 0.0 | 754.0 | 2.9 | 0.0 |
----
-## 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`. 3 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 UNICEF Democratic Republic of Congo 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/prevalence-de-la-malnutrition-en-rdc) for the publisher's own methodology notes and caveats.
----
 ## Citation
 ```bibtex
-@dataset{hdx_africa_prevalence_de_la_malnutrition_en_rdc,
-  title     = {Démocratique République du Congo : Prévalence de la malnutrition},
-  author    = {UNICEF Democratic Republic of Congo},
-  year      = {2025},
-  url       = {https://data.humdata.org/dataset/prevalence-de-la-malnutrition-en-rdc},
-  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.*

 ---
+license: cc-by-4.0
 language:
 - en
 task_categories:
 - tabular-classification
 - tabular-regression
+multilinguality: monolingual
+size_categories:
+- n<1K
 tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "health"
+- "parquet"
+- "tabular"
+- "text"
+- "humanitarian"
+- "hdx"
+- "global-acute-malnutrition-gam"
+- "hxl"
+- "malnutrition"
+- "nutrition"
+- "severe-acute-malnutrition-sam"
+- "cod"
+pretty_name: "Démocratique République du Congo : Prévalence de la malnutrition | Africa (original)"
 ---
+# Démocratique République du Congo : Prévalence de la malnutrition | Africa (original)
+**Size category:** `n<1K` - **Formats:** `parquet` - **Sector:** health - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+![size](https://img.shields.io/badge/size-n%3C1K-blue)
+![sector](https://img.shields.io/badge/sector-health-green)
+![downloads](https://img.shields.io/badge/HF_downloads-59-orange)
+![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey)
+## TL;DR
+This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
+## What This Dataset Covers
+Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance.
+Dataset context from the existing Hugging Face card: Démocratique République du Congo : Prévalence de la malnutrition Publisher: UNICEF Democratic Republic of Congo · Source: HDX · License: cc-by-igo · Updated: 2025-05-05 Abstract Prévalence de la malnutrition pour les enfants et les Femmes enceintes et allaitantes désagrégés par zone de santé. Les données sont à jours Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2025-05-05. Geographic scope: COD. Curated… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-prevalence-de-la-malnutrition-en-rdc.
+## Dataset Profile
+| Field | Value |
 |---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-prevalence-de-la-malnutrition-en-rdc`](https://huggingface.co/datasets/electricsheepafrica/africa-prevalence-de-la-malnutrition-en-rdc) |
+| Sector | health |
+| Topic tags | humanitarian, hdx, electric-sheep-africa, global-acute-malnutrition-gam, hxl, malnutrition, nutrition, severe-acute-malnutrition-sam, cod |
+| Modalities | `tabular`, `text` |
+| Formats | `parquet` |
+| Size category | `n<1K` |
+| Countries | Africa-wide or source-defined African coverage |
+| ISO3 coverage | `not declared` |
+| Last modified on HF | `2026-04-17 05:13:59+00:00` |
+| Inventory snapshot | `2026-07-16T16:00:34Z` |
+## How To Read This Dataset
+- Start from the repository files and the dataset viewer when available.
+- Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
+- Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
+- Preserve missing values until you have a defensible imputation rule.
+## Usage
 ```python
 from datasets import load_dataset
+ds = load_dataset("electricsheepafrica/africa-prevalence-de-la-malnutrition-en-rdc")
+print(ds)
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
 ```
+### Convert To Pandas When Tabular
+```python
+from datasets import Dataset
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
+```
+## Data Quality Notes
+- This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
+- Exact schema, row counts, and source files should be inspected in the repository data files.
+- Metadata gaps from the inventory: country, upstream_publisher.
+- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.
+## Source And Provenance
+- **Source context:** original
+- **Publisher/source attribution:** original
+- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-prevalence-de-la-malnutrition-en-rdc](https://huggingface.co/datasets/electricsheepafrica/africa-prevalence-de-la-malnutrition-en-rdc)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
+## Suggested Analyses
+- Inspect schema and missingness before modeling.
+- Profile variables by geography, time, and subgroup columns where present.
+- Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
+- Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.
 ## Citation
 ```bibtex
+@misc{electric_sheep_africa_africa_prevalence_de_la_malnutrition_en_rdc_2026,
+  title        = {Démocratique République du Congo : Prévalence de la malnutrition | Africa (original)},
+  author       = {original},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-prevalence-de-la-malnutrition-en-rdc},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-prevalence-de-la-malnutrition-en-rdc}}
 }
 ```
+## License
+Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
+Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.
+## About Electric Sheep Africa
+Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.
 ---
+Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.