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

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- food-security
- moz
pretty_name: "Mozambique Current Situation FEWS NET Acute Food Insecurity Classifications Data"
dataset_info:
splits:
- name: train
num_examples: 7269
- name: test
num_examples: 1817
---

# Mozambique Current Situation FEWS NET Acute Food Insecurity Classifications Data

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

---

## Abstract

Mozambique 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: **MOZ**.

*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)** | 9,087 |
| **Columns** | 40 (9 numeric, 23 categorical, 7 datetime) |
| **Train split** | 7,269 rows |
| **Test split** | 1,817 rows |
| **Geographic scope** | MOZ |
| **Publisher** | FEWS NET |
| **HDX last updated** | 2026-04-01 |

---

## Variables

**Geographic** — `country` (Mozambique), `country_code` (MZ), `fewsnet_region` (Southern Africa), `unit_type` (fsc_admin_lhz, fsc_admin), `specialization_type` and 2 others.

**Temporal** — `datacollectionperiod` (range 158531.0–377925.0), `reporting_date`.

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

**Identifier / Metadata** — `source_organization` (FEWS NET, Mozambique), `source_document` (Food Security Outlook, Mozambique), `geographic_unit_full_name` (Alto Molocue, Zambezia, Mozambique, Mecufi, Cabo Delgado, Mozambique, Massingir, Gaza, Mozambique), `geographic_unit_name` (North-Central Coastal Fishing, Northern Highland with Mixed Cropping, Northeastern Cassava, Cashew, and Coconut), `fnid` (MZ2009C11101, MZ2009C10210, MZ2009C10208) and 8 others.

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

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mozambique-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, Mozambique |
| `source_document` | object | 0.0% | Food Security Outlook, Mozambique |
| `country` | object | 0.0% | Mozambique |
| `country_code` | object | 0.0% | MZ |
| `geographic_group` | object | 0.0% | Eastern Africa |
| `fewsnet_region` | object | 0.0% | Southern Africa |
| `geographic_unit_full_name` | object | 0.0% | Alto Molocue, Zambezia, Mozambique, Mecufi, Cabo Delgado, Mozambique, Massingir, Gaza, Mozambique |
| `geographic_unit_name` | object | 0.0% | North-Central Coastal Fishing, Northern Highland with Mixed Cropping, Northeastern Cassava, Cashew, and Coconut |
| `unit_type` | object | 0.0% | fsc_admin_lhz, fsc_admin |
| `fnid` | object | 0.0% | MZ2009C11101, MZ2009C10210, MZ2009C10208 |
| `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 1.4425) |
| `description` | object | 0.0% | |
| `id` | int64 | 0.0% | 24359209.0 – 41422894.0 (mean 27458095.2796) |
| `datacollectionperiod` | int64 | 0.0% | 158531.0 – 377925.0 (mean 210719.0898) |
| `datacollection` | int64 | 0.0% | 168258.0 – 388759.0 (mean 221782.5272) |
| `scenario` | object | 0.0% | |
| `geographic_unit` | int64 | 0.0% | 24965.0 – 203435.0 (mean 101794.9464) |
| `datasourceorganization` | int64 | 0.0% | 2031.0 – 2031.0 (mean 2031.0) |
| `datasourcedocument` | int64 | 0.0% | 6565.0 – 6565.0 (mean 6565.0) |
| `dataseries` | int64 | 0.0% | 6472963.0 – 7847754.0 (mean 6585353.9275) |
| `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 | 1.4425 | 1.0 |
| `id` | 24359209.0 | 41422894.0 | 27458095.2796 | 24520509.0 |
| `datacollectionperiod` | 158531.0 | 377925.0 | 210719.0898 | 159580.0 |
| `datacollection` | 168258.0 | 388759.0 | 221782.5272 | 168722.0 |
| `geographic_unit` | 24965.0 | 203435.0 | 101794.9464 | 87808.0 |
| `datasourceorganization` | 2031.0 | 2031.0 | 2031.0 | 2031.0 |
| `datasourcedocument` | 6565.0 | 6565.0 | 6565.0 | 6565.0 |
| `dataseries` | 6472963.0 | 7847754.0 | 6585353.9275 | 6507257.0 |

---

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

---

## Citation

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