electricsheepafrica/africa-somalia-acute-malnutrition-analysis

Somalia : Acute Malnutrition

Publisher: HDX · Source: HDX · 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.

---

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

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 for the publisher's own methodology notes and caveats.

---

Citation

@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 — Africa's ML dataset infrastructure. Lagos, Nigeria.