README.md

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- food-security
- integrated-food-security-phase-classification-ipc
- cmr
pretty_name: "Cameroon: Acute Food Insecurity Country Data"
dataset_info:
  splits:
    - name: train
      num_examples: 11
    - name: test
      num_examples: 2
---

# Cameroon: Acute Food Insecurity Country Data

**Publisher:** Integrated Food Security Phase Classification (IPC) · **Source:** [HDX](https://data.humdata.org/dataset/cameroon-acute-food-insecurity-country-data) · **License:** `other-pd-nr` · **Updated:** 2026-02-16

---

## Abstract

The IPC Acute Food Insecurity (IPC AFI) classification provides strategically relevant information to decision makers that focuses on short-term objectives to prevent, mitigate or decrease severe food insecurity that threatens lives or livelihoods. This data has been produced by the National IPC Technical Working Groups for IPC population estimates since 2017. All national population figures are based on official country population estimates. IPC estimates are those published in country IPC reports.  
  
There is also a [global dataset](https://data.humdata.org/dataset/global-acute-food-insecurity-country-data).

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the `date_of_analysis`, `from` column(s). Geographic scope: **CMR**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Food security and nutrition |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 14 |
| **Columns** | 11 (3 numeric, 5 categorical, 3 datetime) |
| **Train split** | 11 rows |
| **Test split** | 2 rows |
| **Geographic scope** | CMR |
| **Publisher** | Integrated Food Security Phase Classification (IPC) |
| **HDX last updated** | 2026-02-16 |

---

## Variables

**Geographic** — `date_of_analysis`, `country` (CMR), `total_country_population` (range 29414763.0–29414763.0), `validity_period` (current, first projection).

**Demographic** — `percentage` (range 0.0–1.0).

**Outcome / Measurement** — `phase` (all, 3+, 1), `number` (range 0.0–29414764.0).

**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-04).

**Other** — `from`, `to`.

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-cameroon-acute-food-insecurity-country-data")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

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

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `date_of_analysis` | datetime64[ns] | 0.0% |  |
| `country` | object | 0.0% | CMR |
| `total_country_population` | int64 | 0.0% | 29414763.0 – 29414763.0 (mean 29414763.0) |
| `validity_period` | object | 0.0% | current, first projection |
| `from` | datetime64[ns] | 0.0% |  |
| `to` | datetime64[ns] | 0.0% |  |
| `phase` | object | 0.0% | all, 3+, 1 |
| `number` | int64 | 0.0% | 0.0 – 29414764.0 (mean 8831254.7857) |
| `percentage` | float64 | 0.0% | 0.0 – 1.0 (mean 0.3007) |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-04 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `total_country_population` | 29414763.0 | 29414763.0 | 29414763.0 | 29414763.0 |
| `number` | 0.0 | 29414764.0 | 8831254.7857 | 2996617.0 |
| `percentage` | 0.0 | 1.0 | 0.3007 | 0.105 |

---

## 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`. 3 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 Integrated Food Security Phase Classification (IPC) 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/cameroon-acute-food-insecurity-country-data) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_cameroon_acute_food_insecurity_country_data,
  title     = {Cameroon: Acute Food Insecurity Country Data},
  author    = {Integrated Food Security Phase Classification (IPC)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/cameroon-acute-food-insecurity-country-data},
  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.*