Add README.md

README.md

@@ -1,108 +1,192 @@
 ---
+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
+- other
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- food-security
+- cod
+pretty_name: "Congo, The Democratic Republic of the Current Situation FEWS NET Acutely Food Insecure Population Estimates Data"
 dataset_info:
-  features:
-  - name: source_organization
-    dtype: string
-  - name: source_document
-    dtype: string
-  - name: country
-    dtype: string
-  - name: country_code
-    dtype: string
-  - name: geographic_group
-    dtype: string
-  - name: fewsnet_region
-    dtype: string
-  - name: geographic_unit_full_name
-    dtype: string
-  - name: geographic_unit_name
-    dtype: string
-  - name: fnid
-    dtype: string
-  - name: admin_0
-    dtype: string
-  - name: phase
-    dtype: string
-  - name: scenario_name
-    dtype: string
-  - name: indicator_name
-    dtype: string
-  - name: indicator_abbreviation
-    dtype: string
-  - name: projection_start
-    dtype: timestamp[ns]
-  - name: projection_end
-    dtype: timestamp[ns]
-  - name: status
-    dtype: string
-  - name: low_value
-    dtype: float64
-  - name: high_value
-    dtype: float64
-  - name: value
-    dtype: float64
-  - name: id
-    dtype: int64
-  - name: datacollectionperiod
-    dtype: int64
-  - name: datacollection
-    dtype: int64
-  - name: scenario
-    dtype: string
-  - name: geographic_unit
-    dtype: int64
-  - name: datasourceorganization
-    dtype: int64
-  - name: datasourcedocument
-    dtype: int64
-  - name: dataseries
-    dtype: int64
-  - name: dataseries_name
-    dtype: string
-  - name: specialization_type
-    dtype: string
-  - name: dataseries_specialization_type
-    dtype: string
-  - name: data_usage_policy
-    dtype: string
-  - name: created
-    dtype: timestamp[ns]
-  - name: modified
-    dtype: timestamp[ns]
-  - name: status_changed
-    dtype: timestamp[ns]
-  - name: collection_status
-    dtype: string
-  - name: collection_status_changed
-    dtype: timestamp[ns]
-  - name: collection_schedule
-    dtype: string
-  - name: reporting_date
-    dtype: timestamp[ns]
-  - name: phase_name
-    dtype: string
-  - name: population_range
-    dtype: string
-  - name: description
-    dtype: string
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 41676
-    num_examples: 60
-  - name: test
-    num_bytes: 10422
-    num_examples: 15
-  download_size: 45778
-  dataset_size: 52098
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 60
+    - name: test
+      num_examples: 15
 ---
+
+# Congo, The Democratic Republic of the Current Situation FEWS NET Acutely Food Insecure Population Estimates Data
+
+**Publisher:** FEWS NET · **Source:** [HDX](https://data.humdata.org/dataset/congo__the_democratic_republic_of_the_current_situation_fewsnet_fipe) · **License:** `cc-by` · **Updated:** 2026-04-01
+
+---
+
+## Abstract
+
+Congo, The Democratic Republic of the Current Situation FEWS NET Acutely Food Insecure Population Estimates Data from 2019
+
+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: **COD**.
+
+*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)** | 75 |
+| **Columns** | 44 (10 numeric, 27 categorical, 7 datetime) |
+| **Train split** | 60 rows |
+| **Test split** | 15 rows |
+| **Geographic scope** | COD |
+| **Publisher** | FEWS NET |
+| **HDX last updated** | 2026-04-01 |
+
+---
+
+## Variables
+
+**Geographic** — `country` (Democratic Republic of the Congo), `country_code` (CD), `fewsnet_region` (Southern Africa), `admin_0` (Democratic Republic of Congo), `specialization_type` and 3 others.
+
+**Temporal** — `datacollectionperiod` (range 310322.0–373072.0), `reporting_date`.
+
+**Outcome / Measurement** — `phase`, `low_value` (range 2500000.0–16000000.0), `high_value` (range 4999999.0–16999999.0), `value` (range 2500000.0–16000000.0), `phase_name`.
+
+**Identifier / Metadata** — `source_organization` (FEWS NET), `source_document` (Food Assistance Outlook Brief), `geographic_unit_full_name` (Congo, The Democratic Republic of the), `geographic_unit_name` (Democratic Republic of Congo), `fnid` (CD) and 8 others.
+
+**Other** — `geographic_group` (Middle Africa), `indicator_abbreviation`, `projection_start`, `projection_end`, `status` and 11 others.
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-congo-the-democratic-republic-of-the-current-situation-fewsnet-fipe")
+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 |
+| `source_document` | object | 0.0% | Food Assistance Outlook Brief |
+| `country` | object | 0.0% | Democratic Republic of the Congo |
+| `country_code` | object | 0.0% | CD |
+| `geographic_group` | object | 0.0% | Middle Africa |
+| `fewsnet_region` | object | 0.0% | Southern Africa |
+| `geographic_unit_full_name` | object | 0.0% | Congo, The Democratic Republic of the |
+| `geographic_unit_name` | object | 0.0% | Democratic Republic of Congo |
+| `fnid` | object | 0.0% | CD |
+| `admin_0` | object | 0.0% | Democratic Republic of Congo |
+| `phase` | object | 0.0% |  |
+| `scenario_name` | object | 0.0% |  |
+| `indicator_name` | object | 0.0% |  |
+| `indicator_abbreviation` | object | 0.0% |  |
+| `projection_start` | datetime64[ns] | 0.0% |  |
+| `projection_end` | datetime64[ns] | 0.0% |  |
+| `status` | object | 0.0% |  |
+| `low_value` | float64 | 0.0% | 2500000.0 – 16000000.0 (mean 7566666.6667) |
+| `high_value` | float64 | 0.0% | 4999999.0 – 16999999.0 (mean 9586665.6667) |
+| `value` | float64 | 0.0% | 2500000.0 – 16000000.0 (mean 7566666.6667) |
+| `id` | int64 | 0.0% | 33126743.0 – 40657252.0 (mean 34149603.4) |
+| `datacollectionperiod` | int64 | 0.0% | 310322.0 – 373072.0 (mean 319279.3333) |
+| `datacollection` | int64 | 0.0% | 325936.0 – 383437.0 (mean 333815.6) |
+| `scenario` | object | 0.0% |  |
+| `geographic_unit` | int64 | 0.0% | 8023.0 – 8023.0 (mean 8023.0) |
+| `datasourceorganization` | int64 | 0.0% | 1.0 – 1.0 (mean 1.0) |
+| `datasourcedocument` | int64 | 0.0% | 6986.0 – 6986.0 (mean 6986.0) |
+| `dataseries` | int64 | 0.0% | 6932788.0 – 6932788.0 (mean 6932788.0) |
+| `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% |  |
+| `phase_name` | object | 0.0% |  |
+| `population_range` | object | 0.0% |  |
+| `description` | object | 0.0% |  |
+| `esa_source` | object | 0.0% |  |
+| `esa_processed` | object | 0.0% |  |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `low_value` | 2500000.0 | 16000000.0 | 7566666.6667 | 7500000.0 |
+| `high_value` | 4999999.0 | 16999999.0 | 9586665.6667 | 9999999.0 |
+| `value` | 2500000.0 | 16000000.0 | 7566666.6667 | 7500000.0 |
+| `id` | 33126743.0 | 40657252.0 | 34149603.4 | 33128853.0 |
+| `datacollectionperiod` | 310322.0 | 373072.0 | 319279.3333 | 310396.0 |
+| `datacollection` | 325936.0 | 383437.0 | 333815.6 | 325973.0 |
+| `geographic_unit` | 8023.0 | 8023.0 | 8023.0 | 8023.0 |
+| `datasourceorganization` | 1.0 | 1.0 | 1.0 | 1.0 |
+| `datasourcedocument` | 6986.0 | 6986.0 | 6986.0 | 6986.0 |
+| `dataseries` | 6932788.0 | 6932788.0 | 6932788.0 | 6932788.0 |
+
+---
+
+## 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`. 7 column(s) with >80% missing values were removed: `admin_1`, `admin_2`, `admin_3`, `admin_4`, `pct_phase3`, `pct_phase4`.... 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/congo__the_democratic_republic_of_the_current_situation_fewsnet_fipe) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_congo_the_democratic_republic_of_the_current_situation_fewsnet_fipe,
+  title     = {Congo, The Democratic Republic of the Current Situation FEWS NET Acutely Food Insecure Population Estimates Data},
+  author    = {FEWS NET},
+  year      = {2026},
+  url       = {https://data.humdata.org/dataset/congo__the_democratic_republic_of_the_current_situation_fewsnet_fipe},
+  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.*