electricsheepasia/asia-malnutrition-afghanistan-malnutrition-prevalence

Afghanistan - Malnutrition Prevalence

Publisher: OCHA Afghanistan · Source: HDX · License: cc-by · Updated: 2025-05-05

Abstract

Afghanistan prevalence of malnutrition, SAM, MAM and GAM.

Each row in this dataset represents tabular records. Data was last updated on HDX on 2025-05-05. Geographic scope: AFG.

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

Dataset Characteristics

Variables

Geographic — wasting_by_weight_for_height_z_score_oedema_criteria (range 486.0–1271.0), underweight_by_weight_for_age_z_score_criteria (range 34.0–1276.0), stunting_by_height_for_age_z_score_criteria (range 473.0–1262.0).

Identifier / Metadata — unnamed_0 (Province, Parwan, Kabul Rural), unnamed_1 (AF01, PROVCODE, AF03), `unnamed3 (range 1.0–29.0), unnamed4` (1.8 (1.1-3.0), % (95% CI), 2.1 (1.3-3.4)), `unnamed5` (range 9.0–127.0) and 23 others.

Other — combined_gam_and_sam_based_on_whz_and_muac (range 494.0–1282.0).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-malnutrition-afghanistan-malnutrition-prevalence")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Numeric Summary

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. 16 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 OCHA Afghanistan 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_asia_malnutrition_afghanistan_malnutrition_prevalence,
  title     = {Afghanistan - Malnutrition Prevalence},
  author    = {OCHA Afghanistan},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/afghanistan-malnutrition-prevalence},
  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.