---
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
- global-acute-malnutrition-gam
- malnutrition
- nutrition
- severe-acute-malnutrition-sam
- zwe
pretty_name: "Zimbabwe: GAM, MAM and SAM based on MUAC measurements"
dataset_info:
  splits:
    - name: train
      num_examples: 73
    - name: test
      num_examples: 18
---

# Zimbabwe: GAM, MAM and SAM based on MUAC measurements

**Publisher:** OCHA Regional Office for Southern and Eastern Africa (ROSEA) · **Source:** [HDX](https://data.humdata.org/dataset/zimbabwe-gam-mam-and-sam-based-on-muac-measurements) · **License:** `cc-by` · **Updated:** 2025-04-10

---

## Abstract

GAM,MAM and SAM for Zimbabwe based on MUAC measurements

Each row in this dataset represents geolocated point observations. Data was last updated on HDX on 2025-04-10. Geographic scope: **ZWE**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Demographics and population |
| **Unit of observation** | Geolocated point observations |
| **Rows (total)** | 92 |
| **Columns** | 7 (3 numeric, 4 categorical, 0 datetime) |
| **Train split** | 73 rows |
| **Test split** | 18 rows |
| **Geographic scope** | ZWE |
| **Publisher** | OCHA Regional Office for Southern and Eastern Africa (ROSEA) |
| **HDX last updated** | 2025-04-10 |

---

## Variables

**Geographic** — `population_group` (Children under 5 years).

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-19).

**Other** — `adm_2` (Bulawayo, Zaka, Victoria Falls), `sam` (range 0.0–0.047), `mam` (range 0.0–0.105), `gam` (range 0.0–0.112).

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-zimbabwe-gam-mam-and-sam-based-on-muac-measurements")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `adm_2` | object | 0.0% | Bulawayo, Zaka, Victoria Falls |
| `population_group` | object | 0.0% | Children under 5 years |
| `sam` | float64 | 0.0% | 0.0 – 0.047 (mean 0.0155) |
| `mam` | float64 | 0.0% | 0.0 – 0.105 (mean 0.0139) |
| `gam` | float64 | 0.0% | 0.0 – 0.112 (mean 0.0294) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-19 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `sam` | 0.0 | 0.047 | 0.0155 | 0.013 |
| `mam` | 0.0 | 0.105 | 0.0139 | 0.009 |
| `gam` | 0.0 | 0.112 | 0.0294 | 0.025 |

---

## 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 OCHA Regional Office for Southern and Eastern Africa (ROSEA) 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/zimbabwe-gam-mam-and-sam-based-on-muac-measurements) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_zimbabwe_gam_mam_and_sam_based_on_muac_measurements,
  title     = {Zimbabwe: GAM, MAM and SAM based on MUAC measurements},
  author    = {OCHA Regional Office for Southern and Eastern Africa (ROSEA)},
  year      = {2025},
  url       = {https://data.humdata.org/dataset/zimbabwe-gam-mam-and-sam-based-on-muac-measurements},
  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.*