electricsheepafrica/africa-somalia-acute-malnutrition-analysis

---
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
- children
- health
- indicators
- nutrition
- som
pretty_name: "Somalia : Acute Malnutrition"
dataset_info:
splits:
- name: train
num_examples: 15
- name: test
num_examples: 3
---

# Somalia : Acute Malnutrition

**Publisher:** HDX · **Source:** [HDX](https://data.humdata.org/dataset/somalia-acute-malnutrition-analysis) · **License:** `cc-by` · **Updated:** 2025-10-21

---

## Abstract

The dataset shows Global Acute Malnutrition (GAM) moderate acute malnutrition (MAM) and severe acute malnutrition (SAM) numbers in Somalia.

Each row in this dataset represents time-series observations. Data was last updated on HDX on 2025-10-21. Geographic scope: **SOM**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Time-series observations |
| **Rows (total)** | 19 |
| **Columns** | 7 (0 numeric, 7 categorical, 0 datetime) |
| **Train split** | 15 rows |
| **Test split** | 3 rows |
| **Geographic scope** | SOM |
| **Publisher** | HDX |
| **HDX last updated** | 2025-10-21 |

---

## Variables

**Temporal** — `no_of_children_6_59_months_in_need_of_treatment` (Moderate Acute
Malnutrition
(MAM), 40,270, 27,820).

**Identifier / Metadata** — `unnamed_0` (Region, Nugaal, Middle Juba), `unnamed_1` (Children
6-59
months, 126,362, 86,026), `unnamed_3` (Severer Acute
Malnutrition
(SAM), 9,350, 9,510), `unnamed_4` (Global
Malnutrition
(GAM), 49,620, 37,330), `esa_source` (HDX) and 1 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-somalia-acute-malnutrition-analysis")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `unnamed_0` | object | 0.0% | Region, Nugaal, Middle Juba |
| `unnamed_1` | object | 0.0% | Children
6-59
months, 126,362, 86,026 |
| `no_of_children_6_59_months_in_need_of_treatment` | object | 0.0% | Moderate Acute
Malnutrition
(MAM), 40,270, 27,820 |
| `unnamed_3` | object | 0.0% | Severer Acute
Malnutrition
(SAM), 9,350, 9,510 |
| `unnamed_4` | object | 0.0% | Global
Malnutrition
(GAM), 49,620, 37,330 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-07 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
_No numeric columns._

---

## 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 HDX 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/somalia-acute-malnutrition-analysis) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_somalia_acute_malnutrition_analysis,
title = {Somalia : Acute Malnutrition},
author = {HDX},
year = {2025},
url = {https://data.humdata.org/dataset/somalia-acute-malnutrition-analysis},
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.*