electricsheepafrica/africa-drc-3-w-national-octobre-2015

---
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
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- education
- who-is-doing-what-and-where-3w-4w-5w
- cod
pretty_name: "DRC - 3 W National - Novembre 2015"
dataset_info:
splits:
- name: train
num_examples: 1041
- name: test
num_examples: 260
---

# DRC - 3 W National - Novembre 2015

**Publisher:** OCHA Democratic Republic of the Congo (DRC) · **Source:** [HDX](https://data.humdata.org/dataset/drc-3-w-national-octobre-2015) · **License:** `cc-by-igo` · **Updated:** 2025-09-28

---

## Abstract

Dans ce fichier, vous trouverai les organisations ainsi que leurs projets et autres détails important.

Each row in this dataset represents first-level administrative unit observations. Temporal coverage is indicated by the `start_date`, `end_date` column(s). Geographic scope: **COD**.

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

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Public health |
| **Unit of observation** | First-level administrative unit observations |
| **Rows (total)** | 1,302 |
| **Columns** | 22 (2 numeric, 18 categorical, 2 datetime) |
| **Train split** | 1,041 rows |
| **Test split** | 260 rows |
| **Geographic scope** | COD |
| **Publisher** | OCHA Democratic Republic of the Congo (DRC) |
| **HDX last updated** | 2025-09-28 |

---

## Variables

**Geographic** — `agency_acronym` (IRC, IEDA Relief, AVSI), `province_adm1` (Sud-Kivu, Nord-Kivu, Ituri), `territory_adm3` (Masisi, Irumu, Fizi), `location` (Masisi, Bukavu, Lubumbashi), `activity_description` (Monitoring de protection, Soins de santé primaire, rehabilitation, appui institutionnel, Médiation / transformation des conflits / Dialogue Intercommunautaire) and 1 others.

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

**Demographic** — `household`.

**Identifier / Metadata** — `adm1pcode` (range 61.0–707.0), `adm3pcode` (range 5022.0–7075.0), `esa_source`, `esa_processed`.

**Other** — `health_division` (Manono, Kalemie, Fizi), `titre_de_projet` (Intrants agricoles et semences, BCNUDH coordonne la réponse apportée par les sections pertinentes de la MONUSCO, notamment la brigade du Sud-Kivu qui dispose d’un mandat de protection par la présence, à des menaces de protection données, Suivi et Evaluation des activités relevant du mandat du HCR), `cluster` (Protection, Santé, Sécurité Alimentaire), `sub_cluster` (Sensibilisations & formations - Lutte contre les violences sexuelles, Prise en charge de la malNutrition aigüe modérée, Santé), `project` (Humanitaire, Développement, Urgence) and 4 others.

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-drc-3-w-national-octobre-2015")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `agency_acronym` | object | 0.0% | IRC, IEDA Relief, AVSI |
| `province_adm1` | object | 0.0% | Sud-Kivu, Nord-Kivu, Ituri |
| `adm1pcode` | int64 | 0.0% | 61.0 – 707.0 (mean 294.0837) |
| `territory_adm3` | object | 0.0% | Masisi, Irumu, Fizi |
| `adm3pcode` | int64 | 0.0% | 5022.0 – 7075.0 (mean 6148.9501) |
| `health_division` | object | 10.7% | Manono, Kalemie, Fizi |
| `location` | object | 32.7% | Masisi, Bukavu, Lubumbashi |
| `titre_de_projet` | object | 0.0% | Intrants agricoles et semences, BCNUDH coordonne la réponse apportée par les sections pertinentes de la MONUSCO, notamment la brigade du Sud-Kivu qui dispose d’un mandat de protection par la présence, à des menaces de protection données, Suivi et Evaluation des activités relevant du mandat du HCR |
| `cluster` | object | 0.0% | Protection, Santé, Sécurité Alimentaire |
| `sub_cluster` | object | 72.8% | Sensibilisations & formations - Lutte contre les violences sexuelles, Prise en charge de la malNutrition aigüe modérée, Santé |
| `activity_description` | object | 57.3% | Monitoring de protection, Soins de santé primaire, rehabilitation, appui institutionnel, Médiation / transformation des conflits / Dialogue Intercommunautaire |
| `project` | object | 26.7% | Humanitaire, Développement, Urgence |
| `project_description` | object | 17.8% | |
| `donors` | object | 24.6% | |
| `household` | object | 47.8% | |
| `type_of_beneficiary` | object | 12.5% | |
| `implenting_partners` | object | 76.9% | |
| `start_date` | datetime64[ns] | 21.3% | |
| `end_date` | datetime64[ns] | 23.0% | |
| `notes` | object | 70.4% | |
| `esa_source` | object | 0.0% | |
| `esa_processed` | object | 0.0% | |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `adm1pcode` | 61.0 | 707.0 | 294.0837 | 62.0 |
| `adm3pcode` | 5022.0 | 7075.0 | 6148.9501 | 6222.0 |

---

## 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: `sub_type_project`, `women`, `men`. 12 exact duplicate rows were removed. 2 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 OCHA Democratic Republic of the Congo (DRC) and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- The following columns have >20% missing values and should be treated with caution in modelling: `location`, `sub_cluster`, `activity_description`, `project`, `donors`, `household`, `implenting_partners`, `start_date`....
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/drc-3-w-national-octobre-2015) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_drc_3_w_national_octobre_2015,
title = {DRC - 3 W National - Novembre 2015},
author = {OCHA Democratic Republic of the Congo (DRC)},
year = {2025},
url = {https://data.humdata.org/dataset/drc-3-w-national-octobre-2015},
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.*