electricsheepafrica/africa-world-bank-health-indicators-for-federal-republic-of-somalia

---
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:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- health
- hxl
- indicators
- som
pretty_name: "Federal Republic of Somalia - Health"
dataset_info:
splits:
- name: train
num_examples: 5760
- name: test
num_examples: 1440
---

# Federal Republic of Somalia - Health

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-health-indicators-for-federal-republic-of-somalia) · **License:** `cc-by` · **Updated:** 2025-11-04

---

## Abstract

Contains data from the World Bank's [data portal](http://data.worldbank.org/). There is also a [consolidated country dataset](https://data.humdata.org/dataset/world-bank-combined-indicators-for-federal-republic-of-somalia) on HDX.

Improving health is central to the Millennium Development Goals, and the public sector is the main provider of health care in developing countries. To reduce inequities, many countries have emphasized primary health care, including immunization, sanitation, access to safe drinking water, and safe motherhood initiatives. Data here cover health systems, disease prevention, reproductive health, nutrition, and population dynamics. Data are from the United Nations Population Division, World Health Organization, United Nations Children's Fund, the Joint United Nations Programme on HIV/AIDS, and various other sources.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2025-11-04. Geographic scope: **SOM**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 7,201 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 5,760 rows |
| **Test split** | 1,440 rows |
| **Geographic scope** | SOM |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2025-11-04 |

---

## Variables

**Geographic** — `country_name` (Federal Republic of Somalia, #country+name), `country_iso3` (SOM, #country+code), `year` (range 1960.0–2024.0).

**Outcome / Measurement** — `value` (range -568166.0–19009151.0).

**Identifier / Metadata** — `indicator_name` (Population ages 60-64, male (% of male population), Population ages 35-39, female (% of female population), Population ages 40-44, female (% of female population)), `indicator_code` (SP.POP.6064.MA.5Y, SP.POP.3539.FE.5Y, SP.POP.4044.FE.5Y), `esa_source` (HDX), `esa_processed` (2026-04-08).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-health-indicators-for-federal-republic-of-somalia")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country_name` | object | 0.0% | Federal Republic of Somalia, #country+name |
| `country_iso3` | object | 0.0% | SOM, #country+code |
| `year` | float64 | 0.0% | 1960.0 – 2024.0 (mean 1997.1417) |
| `indicator_name` | object | 0.0% | Population ages 60-64, male (% of male population), Population ages 35-39, female (% of female population), Population ages 40-44, female (% of female population) |
| `indicator_code` | object | 0.0% | SP.POP.6064.MA.5Y, SP.POP.3539.FE.5Y, SP.POP.4044.FE.5Y |
| `value` | float64 | 0.0% | -568166.0 – 19009151.0 (mean 305535.4166) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-08 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2024.0 | 1997.1417 | 2001.0 |
| `value` | -568166.0 | 19009151.0 | 305535.4166 | 24.9727 |

---

## 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`. 2 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 World Bank Group 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/world-bank-health-indicators-for-federal-republic-of-somalia) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_health_indicators_for_federal_republic_of_somalia,
title = {Federal Republic of Somalia - Health},
author = {World Bank Group},
year = {2025},
url = {https://data.humdata.org/dataset/world-bank-health-indicators-for-federal-republic-of-somalia},
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.*