electricsheepafrica/africa-hdro-data-for-cameroon

---
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
- demographics
- development
- education
- gender
- health
- indicators
- socioeconomics
- cmr
pretty_name: "Cameroon - Human Development Indicators"
dataset_info:
splits:
- name: train
num_examples: 750
- name: test
num_examples: 187
---

# Cameroon - Human Development Indicators

**Publisher:** UNDP Human Development Reports Office (HDRO) · **Source:** [HDX](https://data.humdata.org/dataset/hdro-data-for-cameroon) · **License:** `cc-by-igo` · **Updated:** 2026-03-04

---

## Abstract

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities.
The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population.
The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-04. Geographic scope: **CMR**.

*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)** | 938 |
| **Columns** | 10 (2 numeric, 8 categorical, 0 datetime) |
| **Train split** | 750 rows |
| **Test split** | 187 rows |
| **Geographic scope** | CMR |
| **Publisher** | UNDP Human Development Reports Office (HDRO) |
| **HDX last updated** | 2026-03-04 |

---

## Variables

**Geographic** — `country_code` (CMR), `country_name` (Cameroon), `index_id` (GDI, GII, HDI), `index_name` (Gender Development Index, Gender Inequality Index, Human Development Index), `year` (range 1990.0–2023.0).

**Outcome / Measurement** — `value` (range 0.224–5870.496).

**Identifier / Metadata** — `indicator_id` (eys, pop_total, mys_f), `indicator_name` (Expected Years of Schooling (years), Population, total (millions), Mean Years of Schooling, female (years)), `esa_source` (HDX), `esa_processed` (2026-04-08).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-hdro-data-for-cameroon")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `country_code` | object | 0.0% | CMR |
| `country_name` | object | 0.0% | Cameroon |
| `indicator_id` | object | 0.0% | eys, pop_total, mys_f |
| `indicator_name` | object | 0.0% | Expected Years of Schooling (years), Population, total (millions), Mean Years of Schooling, female (years) |
| `index_id` | object | 0.0% | GDI, GII, HDI |
| `index_name` | object | 0.0% | Gender Development Index, Gender Inequality Index, Human Development Index |
| `value` | float64 | 0.0% | 0.224 – 5870.496 (mean 452.5281) |
| `year` | int64 | 0.0% | 1990.0 – 2023.0 (mean 2007.9286) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-08 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `value` | 0.224 | 5870.496 | 452.5281 | 27.143 |
| `year` | 1990.0 | 2023.0 | 2007.9286 | 2009.0 |

---

## 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 UNDP Human Development Reports Office (HDRO) 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/hdro-data-for-cameroon) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_hdro_data_for_cameroon,
title = {Cameroon - Human Development Indicators},
author = {UNDP Human Development Reports Office (HDRO)},
year = {2026},
url = {https://data.humdata.org/dataset/hdro-data-for-cameroon},
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.*