electricsheepafrica/africa-south-sudan-acute-malnutrition

---
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-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- global-acute-malnutrition-gam
- malnutrition
- severe-acute-malnutrition-sam
- ssd
pretty_name: "South Sudan : Acute Malnutrition"
dataset_info:
splits:
- name: train
num_examples: 9
- name: test
num_examples: 2
---

# South Sudan : Acute Malnutrition

**Publisher:** HDX · **Source:** [HDX](https://data.humdata.org/dataset/south-sudan-acute-malnutrition) · **License:** `cc-by` · **Updated:** 2026-02-05

---

## Abstract

South Sudan malnutrition rates from the IPC

Each row in this dataset represents time-series observations. Data was last updated on HDX on 2026-02-05. Geographic scope: **SSD**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Food security and nutrition |
| **Unit of observation** | Time-series observations |
| **Rows (total)** | 12 |
| **Columns** | 8 (0 numeric, 8 categorical, 0 datetime) |
| **Train split** | 9 rows |
| **Test split** | 2 rows |
| **Geographic scope** | SSD |
| **Publisher** | HDX |
| **HDX last updated** | 2026-02-05 |

---

## Variables

**Temporal** — `number_of_children_6_59_months_in` (SAM, 51,760, 43,177).

**Identifier / Metadata** — `unnamed_0` (State, Central Equatoria, Eastern Equatoria), `unnamed_1` (Children 6 - 59
months, 319,004, 229,157), `unnamed_3` (MAM, 136,542, 124,710), `unnamed_4` (GAM, 188,302, 167,887), `unnamed_5` (Pregnant and
Beastfeeding
Women Cases, 80,863, 105,889) and 2 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-south-sudan-acute-malnutrition")
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% | State, Central Equatoria, Eastern Equatoria |
| `unnamed_1` | object | 0.0% | Children 6 - 59
months, 319,004, 229,157 |
| `number_of_children_6_59_months_in` | object | 0.0% | SAM, 51,760, 43,177 |
| `unnamed_3` | object | 0.0% | MAM, 136,542, 124,710 |
| `unnamed_4` | object | 0.0% | GAM, 188,302, 167,887 |
| `unnamed_5` | object | 0.0% | Pregnant and
Beastfeeding
Women Cases, 80,863, 105,889 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-06 |

---

## 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/south-sudan-acute-malnutrition) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_south_sudan_acute_malnutrition,
title = {South Sudan : Acute Malnutrition},
author = {HDX},
year = {2026},
url = {https://data.humdata.org/dataset/south-sudan-acute-malnutrition},
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.*