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

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- food-security
- som
pretty_name: "Somalia Current Situation FEWS NET Acute Food Insecurity Classifications Data"
dataset_info:
splits:
- name: train
num_examples: 9037
- name: test
num_examples: 2259
---

# Somalia Current Situation FEWS NET Acute Food Insecurity Classifications Data

**Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/somalia_current_situation_fewsnet_ipc_classification) · **License:** `cc-by` · **Updated:** 2026-04-01

---

## Abstract

Somalia Current Situation FEWS NET Acute Food Insecurity Classifications Data from 2011

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: **SOM**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Food security and nutrition |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 11,297 |
| **Columns** | 40 (10 numeric, 23 categorical, 7 datetime) |
| **Train split** | 9,037 rows |
| **Test split** | 2,259 rows |
| **Geographic scope** | SOM |
| **Publisher** | FEWS NET |
| **HDX last updated** | 2026-04-01 |

---

## Variables

**Geographic** — `country` (Somalia), `country_code` (SO), `fewsnet_region` (East Africa), `unit_type` (fsc_admin_lhz, idp_camp), `specialization_type` and 2 others.

**Temporal** — `datacollectionperiod` (range 158634.0–378217.0), `reporting_date`.

**Outcome / Measurement** — `value` (range 0.0–5.0).

**Identifier / Metadata** — `source_organization` (FEWS NET, Somalia), `source_document` (Food Security Outlook, Somalia), `geographic_unit_full_name` (Urban, Afgooye, Lower Shabelle, Somalia, Urban, Mogadishu, Banaadir, Somalia, Addun Pastoral, Cadaado, Galgaduud, Somalia), `geographic_unit_name` (Southern Inland Pastoral – Camels, Goats, Sheep, and Cattle, Southern Inland Pastoral: Camel, Sheep & Goats, Hawd Pastoral), `fnid` (SO2016C3190309, SO2016C3230515, SO2016C3200212) and 8 others.

**Other** — `geographic_group` (Eastern Africa), `classification_scale`, `is_allowing_for_assistance` (range 0.0–1.0), `projection_start`, `projection_end` and 12 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-somalia-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, Somalia |
| `source_document` | object | 0.0% | Food Security Outlook, Somalia |
| `country` | object | 0.0% | Somalia |
| `country_code` | object | 0.0% | SO |
| `geographic_group` | object | 0.0% | Eastern Africa |
| `fewsnet_region` | object | 0.0% | East Africa |
| `geographic_unit_full_name` | object | 0.0% | Urban, Afgooye, Lower Shabelle, Somalia, Urban, Mogadishu, Banaadir, Somalia, Addun Pastoral, Cadaado, Galgaduud, Somalia |
| `geographic_unit_name` | object | 0.0% | Southern Inland Pastoral – Camels, Goats, Sheep, and Cattle, Southern Inland Pastoral: Camel, Sheep & Goats, Hawd Pastoral |
| `unit_type` | object | 0.0% | fsc_admin_lhz, idp_camp |
| `fnid` | object | 0.0% | SO2016C3190309, SO2016C3230515, SO2016C3200212 |
| `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` | float64 | 11.4% | 0.0 – 1.0 (mean 0.1449) |
| `projection_start` | datetime64[ns] | 0.0% | |
| `projection_end` | datetime64[ns] | 0.0% | |
| `status` | object | 0.0% | |
| `value` | float64 | 0.0% | 0.0 – 5.0 (mean 2.3281) |
| `description` | object | 0.0% | |
| `id` | int64 | 0.0% | 24372083.0 – 41457433.0 (mean 26827672.1944) |
| `datacollectionperiod` | int64 | 0.0% | 158634.0 – 378217.0 (mean 197785.085) |
| `datacollection` | int64 | 0.0% | 168351.0 – 389003.0 (mean 208014.5078) |
| `scenario` | object | 0.0% | |
| `geographic_unit` | int64 | 0.0% | 26247.0 – 210518.0 (mean 29009.8532) |
| `datasourceorganization` | int64 | 0.0% | 2035.0 – 2035.0 (mean 2035.0) |
| `datasourcedocument` | int64 | 0.0% | 6581.0 – 6581.0 (mean 6581.0) |
| `dataseries` | int64 | 0.0% | 6482878.0 – 7052563.0 (mean 6573602.5688) |
| `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 |
| `is_allowing_for_assistance` | 0.0 | 1.0 | 0.1449 | 0.0 |
| `value` | 0.0 | 5.0 | 2.3281 | 2.0 |
| `id` | 24372083.0 | 41457433.0 | 26827672.1944 | 24630054.0 |
| `datacollectionperiod` | 158634.0 | 378217.0 | 197785.085 | 160154.0 |
| `datacollection` | 168351.0 | 389003.0 | 208014.5078 | 168914.0 |
| `geographic_unit` | 26247.0 | 210518.0 | 29009.8532 | 26759.0 |
| `datasourceorganization` | 2035.0 | 2035.0 | 2035.0 | 2035.0 |
| `datasourcedocument` | 6581.0 | 6581.0 | 6581.0 | 6581.0 |
| `dataseries` | 6482878.0 | 7052563.0 | 6573602.5688 | 6513734.0 |

---

## 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`. 8 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](https://data.humdata.org/dataset/somalia_current_situation_fewsnet_ipc_classification) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_somalia_current_situation_fewsnet_ipc_classification,
title = {Somalia Current Situation FEWS NET Acute Food Insecurity Classifications Data},
author = {FEWS NET},
year = {2026},
url = {https://data.humdata.org/dataset/somalia_current_situation_fewsnet_ipc_classification},
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.*