README.md

---
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
- funding
- cmr
pretty_name: "Cameroon - IFRC Appeals"
dataset_info:
  splits:
    - name: train
      num_examples: 45
    - name: test
      num_examples: 11
---

# Cameroon - IFRC Appeals

**Publisher:** International Federation of Red Cross and Red Crescent Societies (IFRC) · **Source:** [HDX](https://data.humdata.org/dataset/ifrc-appeals-data-for-cameroon) · **License:** `cc-by-igo` · **Updated:** 2026-04-16

---

## Abstract

The International Federation of Red Cross and Red Crescent Societies (IFRC) is the world’s largest humanitarian network. Our secretariat supports local Red Cross and Red Crescent action in more than 192 countries, bringing together almost 15 million volunteers for the good of humanity.  
  
We launch Emergency Appeals for big and complex disasters affecting lots of people who will need long-term support to recover. We also support Red Cross and Red Crescent Societies to respond to lots of small and medium-sized disasters worldwide—through our Disaster Response Emergency Fund (DREF) and in other ways.  
  
There is also a [global dataset](https://data.humdata.org/dataset/global-ifrc-appeals-data).

Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2026-04-16. Geographic scope: **CMR**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Humanitarian and development data |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 57 |
| **Columns** | 41 (14 numeric, 19 categorical, 0 datetime) |
| **Train split** | 45 rows |
| **Test split** | 11 rows |
| **Geographic scope** | CMR |
| **Publisher** | International Federation of Red Cross and Red Crescent Societies (IFRC) |
| **HDX last updated** | 2026-04-16 |

---

## Variables

**Geographic** — `dtype_id` (range 1.0–54.0), `dtype_name` (Epidemic, Flood, Population Movement), `dtype_translation_module_original_language` (en), `atype` (range 0.0–1.0), `atype_display` (DREF, Emergency Appeal) and 18 others.

**Temporal** — `start_date`, `end_date`, `real_data_update`.

**Outcome / Measurement** — `amount_requested` (range 0.0–9600000.0), `amount_funded` (range 0.0–1646503.89).

**Identifier / Metadata** — `aid` (range 9.0–19875.0), `name` (Cameroon - Population Movement, Cameroon - Floods, Cameroon), `code` (MDRCM044, MDRCM013, MDRCM011), `id` (range 3.0–4414.0), `esa_source` and 1 others.

**Other** — `status` (range 0.0–1.0), `sector` (Country cluster for Cameroon, Gabon, Equatorial Guinea and Sao Tome and Principe, Country cluster for Central African Republic and Chad), `created_at`, `modified_at`, `event` (range 97.0–7821.0) and 2 others.

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-ifrc-appeals-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 |
|---|---|---|---|
| `aid` | int64 | 0.0% | 9.0 – 19875.0 (mean 8891.1228) |
| `name` | object | 0.0% | Cameroon - Population Movement, Cameroon - Floods, Cameroon |
| `dtype_id` | int64 | 0.0% | 1.0 – 54.0 (mean 9.0702) |
| `dtype_name` | object | 0.0% | Epidemic, Flood, Population Movement |
| `dtype_translation_module_original_language` | object | 0.0% | en |
| `atype` | int64 | 0.0% | 0.0 – 1.0 (mean 0.2105) |
| `atype_display` | object | 0.0% | DREF, Emergency Appeal |
| `status` | int64 | 0.0% | 0.0 – 1.0 (mean 0.8772) |
| `status_display` | object | 0.0% | Closed, Active |
| `code` | object | 0.0% | MDRCM044, MDRCM013, MDRCM011 |
| `sector` | object | 0.0% | Country cluster for Cameroon, Gabon, Equatorial Guinea and Sao Tome and Principe, Country cluster for Central African Republic and Chad |
| `amount_requested` | float64 | 0.0% | 0.0 – 9600000.0 (mean 706543.0526) |
| `amount_funded` | float64 | 0.0% | 0.0 – 1646503.89 (mean 230490.2099) |
| `start_date` | datetime64[ns, UTC] | 0.0% |  |
| `end_date` | datetime64[ns, UTC] | 0.0% |  |
| `real_data_update` | datetime64[ns, UTC] | 0.0% |  |
| `created_at` | datetime64[ns, UTC] | 0.0% |  |
| `modified_at` | datetime64[ns, UTC] | 0.0% |  |
| `event` | float64 | 1.8% | 97.0 – 7821.0 (mean 2445.4643) |
| `needs_confirmation` | bool | 0.0% |  |
| `country_iso` | object | 0.0% | CM |
| `country_iso3` | object | 0.0% | CMR |
| `country_id` | int64 | 0.0% | 41.0 – 41.0 (mean 41.0) |
| `country_record_type` | int64 | 0.0% | 1.0 – 1.0 (mean 1.0) |
| `country_record_type_display` | object | 0.0% | Country |
| `country_region` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `country_independent` | bool | 0.0% |  |
| `country_is_deprecated` | bool | 0.0% |  |
| `country_fdrs` | object | 0.0% |  |
| `country_name` | object | 0.0% |  |
| `country_society_name` | object | 0.0% |  |
| `country_translation_module_original_language` | object | 0.0% |  |
| `region_name` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `region_id` | int64 | 0.0% | 0.0 – 0.0 (mean 0.0) |
| `region_region_name` | object | 0.0% |  |
| `region_label` | object | 0.0% |  |
| `region_translation_module_original_language` | object | 0.0% |  |
| `id` | int64 | 0.0% | 3.0 – 4414.0 (mean 2313.2456) |
| `initial_num_beneficiaries` | int64 | 0.0% | 0.0 – 3480000.0 (mean 246154.0702) |
| `esa_source` | object | 0.0% |  |
| `esa_processed` | object | 0.0% |  |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `aid` | 9.0 | 19875.0 | 8891.1228 | 8012.0 |
| `dtype_id` | 1.0 | 54.0 | 9.0702 | 5.0 |
| `atype` | 0.0 | 1.0 | 0.2105 | 0.0 |
| `status` | 0.0 | 1.0 | 0.8772 | 1.0 |
| `amount_requested` | 0.0 | 9600000.0 | 706543.0526 | 153062.0 |
| `amount_funded` | 0.0 | 1646503.89 | 230490.2099 | 140914.0 |
| `event` | 97.0 | 7821.0 | 2445.4643 | 1329.0 |
| `country_id` | 41.0 | 41.0 | 41.0 | 41.0 |
| `country_record_type` | 1.0 | 1.0 | 1.0 | 1.0 |
| `country_region` | 0.0 | 0.0 | 0.0 | 0.0 |
| `region_name` | 0.0 | 0.0 | 0.0 | 0.0 |
| `region_id` | 0.0 | 0.0 | 0.0 | 0.0 |
| `id` | 3.0 | 4414.0 | 2313.2456 | 2166.0 |
| `initial_num_beneficiaries` | 0.0 | 3480000.0 | 246154.0702 | 6993.0 |

---

## 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`. 2 column(s) with >80% missing values were removed: `dtype_summary`, `country_average_household_size`. 5 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 International Federation of Red Cross and Red Crescent Societies (IFRC) 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/ifrc-appeals-data-for-cameroon) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_ifrc_appeals_data_for_cameroon,
  title     = {Cameroon - IFRC Appeals},
  author    = {International Federation of Red Cross and Red Crescent Societies (IFRC)},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/ifrc-appeals-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.*