electricsheepafrica/africa-2014-nutrition-smart-survey-results-and-2015-trends

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- nutrition
- mli
pretty_name: "2014 Nutrition SMART Survey results and 2015 trends"
dataset_info:
splits:
- name: train
num_examples: 856
- name: test
num_examples: 214
---

# 2014 Nutrition SMART Survey results and 2015 trends

**Publisher:** OCHA Mali · **Source:** [HDX](https://data.humdata.org/dataset/2014-nutrition-smart-survey-results-and-2015-trends) · **License:** `cc-by-igo` · **Updated:** 2022-09-09

---

## Abstract

The data set contains the results of the 2014 Nutrition SMART Survey and the 2015 projections of the caseload for Mali

Each row in this dataset represents subnational administrative unit observations. Data was last updated on HDX on 2022-09-09. Geographic scope: **MLI**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | Subnational administrative unit observations |
| **Rows (total)** | 1,071 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 856 rows |
| **Test split** | 214 rows |
| **Geographic scope** | MLI |
| **Publisher** | OCHA Mali |
| **HDX last updated** | 2022-09-09 |

---

## Variables

**Geographic** — `region` ( Koulikoro, Sikasso , Kayes), `numero_district` (range 1.0–10.0), `district` (Kayes, Gourma Rharous, Tominian).

**Outcome / Measurement** — `value` (range 0.04–710216.2047).

**Identifier / Metadata** — `variable_short_code` (V01, V10, V16), `esa_source` (HDX), `esa_processed` (2026-04-17).

**Other** — `variable` (Population 2015 variante mediane, CASELOAD pour 2015 (Global 6-59 mois), Femme Enceinte et Allaintante Caseload 100% (BURDEN pour 2015)).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-2014-nutrition-smart-survey-results-and-2015-trends")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `region` | object | 0.0% | Koulikoro, Sikasso , Kayes |
| `numero_district` | int64 | 0.0% | 1.0 – 10.0 (mean 4.3492) |
| `district` | object | 0.0% | Kayes, Gourma Rharous, Tominian |
| `variable_short_code` | object | 0.0% | V01, V10, V16 |
| `variable` | object | 0.0% | Population 2015 variante mediane, CASELOAD pour 2015 (Global 6-59 mois), Femme Enceinte et Allaintante Caseload 100% (BURDEN pour 2015) |
| `value` | float64 | 0.0% | 0.04 – 710216.2047 (mean 23119.4248) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-17 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `numero_district` | 1.0 | 10.0 | 4.3492 | 4.0 |
| `value` | 0.04 | 710216.2047 | 23119.4248 | 1443.0 |

---

## 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 Mali 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/2014-nutrition-smart-survey-results-and-2015-trends) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_2014_nutrition_smart_survey_results_and_2015_trends,
title = {2014 Nutrition SMART Survey results and 2015 trends},
author = {OCHA Mali},
year = {2022},
url = {https://data.humdata.org/dataset/2014-nutrition-smart-survey-results-and-2015-trends},
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.*