electricsheepafrica/africa-cameroon-current-situation-fewsnet-ipc-classification

Cameroon Current Situation FEWS NET Acute Food Insecurity Classifications Data

Publisher: FEWS NET · Source: HDX · License: cc-by · Updated: 2026-04-03

---

Abstract

Cameroon Current Situation FEWS NET Acute Food Insecurity Classifications Data from 2020

Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the projection_start, projection_end column(s). Geographic scope: CMR.

Curated into ML-ready Parquet format by Electric Sheep Africa.

---

Dataset Characteristics

Domain	Food security and nutrition
Unit of observation	First-level administrative unit observations
Rows (total)	1,632
Columns	40 (9 numeric, 23 categorical, 7 datetime)
Train split	1,305 rows
Test split	326 rows
Geographic scope	CMR
Publisher	FEWS NET
HDX last updated	2026-04-03

---

Variables

Geographic — country (Cameroon), country_code (CM), fewsnet_region (West Africa), unit_type (fsc_admin_lhz), specialization_type and 2 others.

Temporal — datacollectionperiod (range 158408.0–374892.0), reporting_date.

Outcome / Measurement — value (range 1.0–3.0).

Identifier / Metadata — source_organization (FEWS NET, Cameroon), source_document (Food Security Outlook, Cameroon), geographic_unit_full_name (ADAMAWA HIGH PLATEUX: cattle, maize, cassava, yams, sweet potatoes, beans, honey, Djerem, Adamawa, Cameroon, ADAMAWA HIGH PLATEUX: cattle, maize, cassava, yams, sweet potatoes, beans, honey, Faro et Deo, Adamawa, Cameroon, TIKAR PLAIN: maize, irrigated rice, robusta coffee, fishing, livestock, Ngo Ketunjia, Northwest, Cameroon), geographic_unit_name (WESTERN HIGHLANDS: maize, market gardening, beans, potatoes, egg production, tubers, arabica coffee, DEGRADED FOREST OF THE CENTER-SOUTH: cocoa, pineapple, cassava, maize, market-gardening, small livestock and poultry, MOUNT CAMEROON FOREST: cocoa, palm oil, robusta coffee, rubber, plantain, tubers, pepper, snails), fnid (CM2020C3010107, CM2020C3010207, CM2020C3070708) and 8 others.

Other — geographic_group (Middle Africa), classification_scale, is_allowing_for_assistance, projection_start, projection_end and 12 others.

---

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-cameroon-current-situation-fewsnet-ipc-classification")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

---

Schema

Column	Type	Null %	Range / Sample Values
source_organization	object	0.0%	FEWS NET, Cameroon
source_document	object	0.0%	Food Security Outlook, Cameroon
country	object	0.0%	Cameroon
country_code	object	0.0%	CM
geographic_group	object	0.0%	Middle Africa
fewsnet_region	object	0.0%	West Africa
geographic_unit_full_name	object	0.0%	ADAMAWA HIGH PLATEUX: cattle, maize, cassava, yams, sweet potatoes, beans, honey, Djerem, Adamawa, Cameroon, ADAMAWA HIGH PLATEUX: cattle, maize, cassava, yams, sweet potatoes, beans, honey, Faro et Deo, Adamawa, Cameroon, TIKAR PLAIN: maize, irrigated rice, robusta coffee, fishing, livestock, Ngo Ketunjia, Northwest, Cameroon
geographic_unit_name	object	0.0%	WESTERN HIGHLANDS: maize, market gardening, beans, potatoes, egg production, tubers, arabica coffee, DEGRADED FOREST OF THE CENTER-SOUTH: cocoa, pineapple, cassava, maize, market-gardening, small livestock and poultry, MOUNT CAMEROON FOREST: cocoa, palm oil, robusta coffee, rubber, plantain, tubers, pepper, snails
unit_type	object	0.0%	fsc_admin_lhz
fnid	object	0.0%	CM2020C3010107, CM2020C3010207, CM2020C3070708
classification_scale	object	0.0%
scenario_name	object	0.0%
preference_rating	int64	0.0%	90.0 – 90.0 (mean 90.0)
is_allowing_for_assistance	bool	0.0%
projection_start	datetime64[ns]	0.0%
projection_end	datetime64[ns]	0.0%
status	object	0.0%
value	float64	0.0%	1.0 – 3.0 (mean 1.5368)
description	object	0.0%
id	int64	0.0%	24352267.0 – 41122665.0 (mean 31266672.6176)
datacollectionperiod	int64	0.0%	158408.0 – 374892.0 (mean 277007.7059)
datacollection	int64	0.0%	168131.0 – 385837.0 (mean 290428.1765)
scenario	object	0.0%
geographic_unit	int64	0.0%	142851.0 – 142946.0 (mean 142898.5)
datasourceorganization	int64	0.0%	2026.0 – 2026.0 (mean 2026.0)
datasourcedocument	int64	0.0%	6533.0 – 6533.0 (mean 6533.0)
dataseries	int64	0.0%	6470274.0 – 6796968.0 (mean 6662438.5588)
dataseries_name	object	0.0%
specialization_type	object	0.0%
dataseries_specialization_type	object	0.0%
data_usage_policy	object	0.0%
created	datetime64[ns]	0.0%
modified	datetime64[ns]	0.0%
status_changed	datetime64[ns]	0.0%
collection_status	object	0.0%
collection_status_changed	datetime64[ns]	0.0%
collection_schedule	object	0.0%
reporting_date	datetime64[ns]	0.0%
esa_source	object	0.0%
esa_processed	object	0.0%

---

Numeric Summary

Column	Min	Max	Mean	Median
preference_rating	90.0	90.0	90.0	90.0
value	1.0	3.0	1.5368	1.0
id	24352267.0	41122665.0	31266672.6176	32087928.5
datacollectionperiod	158408.0	374892.0	277007.7059	292956.0
datacollection	168131.0	385837.0	290428.1765	308377.0
geographic_unit	142851.0	142946.0	142898.5	142898.5
datasourceorganization	2026.0	2026.0	2026.0	2026.0
datasourcedocument	6533.0	6533.0	6533.0	6533.0
dataseries	6470274.0	6796968.0	6662438.5588	6796887.0

---

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. 3 column(s) with >80% missing values were removed: pct_phase3, pct_phase4, pct_phase5. 7 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 FEWS NET 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_cameroon_current_situation_fewsnet_ipc_classification,
  title     = {Cameroon Current Situation FEWS NET Acute Food Insecurity Classifications Data},
  author    = {FEWS NET},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/cameroon_current_situation_fewsnet_ipc_classification},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.