electricsheepafrica/africa-millennium-development-goals

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-classification
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- education
- environment
- facilities-infrastructure
- gender
- gender-and-age-disaggregated-data-gadd
- health
- indicators
- dza
- ago
- ben
- bwa
- bfa
pretty_name: "Africa Millennium Development Goals"
dataset_info:
splits:
- name: train
num_examples: 8488
- name: test
num_examples: 2122
---

# Africa Millennium Development Goals

**Publisher:** African Development Bank Group · **Source:** [HDX](https://data.humdata.org/dataset/africa-millennium-development-goals) · **License:** `cc-by` · **Updated:** 2025-07-22

---

## Abstract

Leadership, innovation and targeted investments in a number of social sectors have led to transformative interventions and in many cases revolutionized people’s lives, says an annual report produced jointly by the Economic Commission for Africa (ECA), the African Union (AU), the African Development Bank (AfDB) and the United Nations Development Programme (UNDP), called “Assessing Progress in Africa Toward the Millennium Development Goals”.

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the `date` column(s). Geographic scope: **DZA, AGO, BEN, BWA, BFA, BDI, CPV, CMR, and 50 others**.

*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)** | 10,610 |
| **Columns** | 14 (5 numeric, 8 categorical, 1 datetime) |
| **Train split** | 8,488 rows |
| **Test split** | 2,122 rows |
| **Geographic scope** | DZA, AGO, BEN, BWA, BFA, BDI, CPV, CMR, and 50 others |
| **Publisher** | African Development Bank Group |
| **HDX last updated** | 2025-07-22 |

---

## Variables

**Geographic** — `countryname` (Mauritius, Congo, Dem. Rep., Chad), `country` (range 1.0–53.0), `frequency` (A).

**Temporal** — `date`.

**Outcome / Measurement** — `value` (range 0.0–4366048.0).

**Identifier / Metadata** — `goalname` (Goal 1: Eradicate extreme poverty and hunger, Goal 7: Ensure environmental sustainability, Goal 2: Achieve universal primary education), `indicatorname` (Employment to population ratio 15-24 yrs (%), Net enrolment ratio in primary education (%), Proportion of population using an improved drinking water source (%)), `social_groupname` (Total, Women, Rural), `esa_source` (HDX), `esa_processed` (2026-04-18).

**Other** — `goal` (KN.1000000, KN.1000060, KN.1000010), `indicator` (range 20265005.0–20270705.0), `social_group` (range 20136605.0–20137105.0), `scale` (range 1.0–1.0).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-millennium-development-goals")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `countryname` | object | 0.0% | Mauritius, Congo, Dem. Rep., Chad |
| `country` | int64 | 0.0% | 1.0 – 53.0 (mean 27.9627) |
| `goalname` | object | 0.0% | Goal 1: Eradicate extreme poverty and hunger, Goal 7: Ensure environmental sustainability, Goal 2: Achieve universal primary education |
| `goal` | object | 0.0% | KN.1000000, KN.1000060, KN.1000010 |
| `indicatorname` | object | 0.0% | Employment to population ratio 15-24 yrs (%), Net enrolment ratio in primary education (%), Proportion of population using an improved drinking water source (%) |
| `indicator` | int64 | 0.0% | 20265005.0 – 20270705.0 (mean 20267660.9943) |
| `social_groupname` | object | 0.0% | Total, Women, Rural |
| `social_group` | int64 | 0.0% | 20136605.0 – 20137105.0 (mean 20137015.82) |
| `scale` | int64 | 0.0% | 1.0 – 1.0 (mean 1.0) |
| `frequency` | object | 0.0% | A |
| `date` | datetime64[ns] | 0.0% | |
| `value` | float64 | 0.0% | 0.0 – 4366048.0 (mean 6365.2114) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-18 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `country` | 1.0 | 53.0 | 27.9627 | 30.0 |
| `indicator` | 20265005.0 | 20270705.0 | 20267660.9943 | 20267305.0 |
| `social_group` | 20136605.0 | 20137105.0 | 20137015.82 | 20137105.0 |
| `scale` | 1.0 | 1.0 | 1.0 | 1.0 |
| `value` | 0.0 | 4366048.0 | 6365.2114 | 39.4 |

---

## 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`. 1 column(s) with >80% missing values were removed: `units`. 1 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 African Development 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.
- This dataset spans 58 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/africa-millennium-development-goals) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_millennium_development_goals,
title = {Africa Millennium Development Goals},
author = {African Development Bank Group},
year = {2025},
url = {https://data.humdata.org/dataset/africa-millennium-development-goals},
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.*