electricsheepafrica/africa-world-bank-health-indicators-for-chad

---
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
- indicators
- tcd
pretty_name: "Chad - Health"
dataset_info:
splits:
- name: train
num_examples: 7812
- name: test
num_examples: 1953
---

# Chad - Health

**Publisher:** World Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/world-bank-health-indicators-for-chad) · **License:** `cc-by` · **Updated:** 2026-03-27

---

## 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-chad) 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 2026-03-27. Geographic scope: **TCD**.

*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)** | 9,765 |
| **Columns** | 8 (2 numeric, 6 categorical, 0 datetime) |
| **Train split** | 7,812 rows |
| **Test split** | 1,953 rows |
| **Geographic scope** | TCD |
| **Publisher** | World Bank Group |
| **HDX last updated** | 2026-03-27 |

---

## Variables

**Geographic** — `country_name` (Chad), `country_iso3` (TCD), `year` (range 1960.0–2025.0).

**Outcome / Measurement** — `value` (range -210126.0–20299123.0).

**Identifier / Metadata** — `indicator_name` (Net migration, Population ages 65 and above, female, Population ages 40-44, male (% of male population)), `indicator_code` (SM.POP.NETM, SP.POP.65UP.FE.IN, SP.POP.4044.MA.5Y), `esa_source` (HDX), `esa_processed` (2026-04-15).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-world-bank-health-indicators-for-chad")
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% | Chad |
| `country_iso3` | object | 0.0% | TCD |
| `year` | int64 | 0.0% | 1960.0 – 2025.0 (mean 1999.0629) |
| `indicator_name` | object | 0.0% | Net migration, Population ages 65 and above, female, Population ages 40-44, male (% of male population) |
| `indicator_code` | object | 0.0% | SM.POP.NETM, SP.POP.65UP.FE.IN, SP.POP.4044.MA.5Y |
| `value` | float64 | 0.0% | -210126.0 – 20299123.0 (mean 221346.4211) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-15 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `year` | 1960.0 | 2025.0 | 1999.0629 | 2003.0 |
| `value` | -210126.0 | 20299123.0 | 221346.4211 | 24.1 |

---

## 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 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-chad) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_world_bank_health_indicators_for_chad,
title = {Chad - Health},
author = {World Bank Group},
year = {2026},
url = {https://data.humdata.org/dataset/world-bank-health-indicators-for-chad},
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.*