electricsheepafrica/africa-north-east-nigeria-global-acute-malnutrition-prevalence-july-2018

North East Nigeria Global Acute Malnutrition Prevalence, July 2018

Publisher: iMMAP Inc. · Source: HDX · License: cc-by · Updated: 2025-04-25

---

Abstract

The zipped CSV files details Global Acute Malnutrition (GAM) prevalence data resulting from Round V of the Nutrition and Food Security SMART survey conducted at the Local Government (LGA) Level in all three crisis-affected states of north east Nigeria, July 2018. Also featured is data on Moderate Acute Malnutrition (MAM), Severe Acute Malnutrition (SAM), and MAM prevalence among beneficiaries 15-49 years, including the respective severity ranking.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-04-25. Geographic scope: NGA.

Curated into ML-ready Parquet format by Electric Sheep Africa.

---

Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	Tabular records
Rows (total)	26
Columns	2 (0 numeric, 2 categorical, 0 datetime)
Train split	20 rows
Test split	5 rows
Geographic scope	NGA
Publisher	iMMAP Inc.
HDX last updated	2025-04-25

---

Variables

Identifier / Metadata — esa_source (HDX), esa_processed (2026-04-12).

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-north-east-nigeria-global-acute-malnutrition-prevalence-july-2018")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
esa_source	object	0.0%	HDX
esa_processed	object	0.0%	2026-04-12

---

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. 1 column(s) with >80% missing values were removed: pk. 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 iMMAP Inc. 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_north_east_nigeria_global_acute_malnutrition_prevalence_july_2018,
  title     = {North East Nigeria Global Acute Malnutrition Prevalence, July 2018},
  author    = {iMMAP Inc.},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/north-east-nigeria-global-acute-malnutrition-prevalence-july-2018},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.