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africa-ifrc-appeals-data-for-cameroon

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Tabular Classification
Tabular Regression
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africa
humanitarian
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funding
cmr
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metadata 
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 · 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.

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.

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 

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 for the publisher's own methodology notes and caveats.

Citation 

@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 — Africa's ML dataset infrastructure. Lagos, Nigeria.