Africa's ML dataset infrastructure.
We build and maintain the largest open collection of machine-learning-ready datasets on the African continent — 7,900+ datasets spanning health, agriculture, energy, finance, education, governance, and more, covering all 54 countries.
Raw data for Africa exists. What hasn't existed — until now — is a single, consistent, ML-ready layer on top of it.
Every dataset in this collection goes through ESA's data pipeline:
Cleaning Missing value markers unified across formats (N/A, null, none, -, unknown, no data, #N/A → NaN). Columns with >80% missingness removed. Duplicate rows and malformed entries resolved.
Normalisation Column names lowercased and snake_cased. Datatypes enforced. Units and categorical encodings standardised across datasets from the same source family.
Augmentation Datasets enriched with contextual features — geographic identifiers, temporal markers, cross-source linkage keys — where source data is sparse or inconsistently structured.
Provenance tracking
Every row carries esa_source and esa_processed fields. Every dataset
ships with a full BibTeX citation traceable to the original publisher.
ML formatting
80/20 train/test splits using a fixed random seed (42). Saved as
Snappy-compressed Parquet. Loadable in one line via the datasets library.
We ingest from the Humanitarian Data Exchange (HDX), Our World in Data (OWID), CGAP, and other primary sources — and do the work that turns raw development data into something a researcher or engineer can actually use.
| Domain | Scope |
|---|---|
| Health & epidemiology | Malaria, HIV, maternal health, mental health, genomics |
| Agriculture | Smallholder surveys, food systems, climate forecasts |
| Energy | Grid infrastructure, electricity access to LGA level |
| Finance | Banking, AML, fintech, microfinance |
| Conflict & displacement | VIEWS forecasts, IDMC displacement data |
| Human development | UNDP HDI indicators, education, poverty |
| Industry | Oil & gas, mining, transport, telecoms, real estate |
Beyond datasets, we build:
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/DATASET_NAME")
train = ds["train"].to_pandas()
To request a dataset not yet in the collection, email kossi@electricsheep.africa.
Year 1 — Foundation & contextual intelligence Building the dataset infrastructure, contextualised models, and research pipelines that establish ESA as Africa's ML data layer.
Year 2 — Systems, deployment & simulation Moving from data to deployed systems: clinical AI, economic simulators, sector-specific models grounded in ESA datasets.
Year 3 — Scale & spin-out Spinning out products and policies built on the foundation. Expanding beyond Nigeria to a continental mandate.
ESA is a non-profit. If our datasets have been useful to your research or product, consider supporting us.
Nigeria — Kuda Bank · Account: 3003437130 · Electric Sheep Africa United States — Lead Bank · Account: 217143145453 · ACH/Wire Routing: 101019644 · Bank address: 1801 Main St., Kansas City, MO 64108
Electric Sheep Africa · Lagos, Nigeria · electricsheep.africa
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。