Add README.md

README.md

@@ -1,54 +1,163 @@
 ---
+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-regression
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- covid-19
+- economics
+- socioeconomics
+- afg
+- alb
+- ago
+- arg
+- arm
+pretty_name: "Compilation of International Financial Institution and Economic Data"
 dataset_info:
-  features:
-  - name: country_code
-    dtype: string
-  - name: country
-    dtype: string
-  - name: world_bank
-    dtype: string
-  - name: ifc
-    dtype: string
-  - name: imf
-    dtype: string
-  - name: afdb
-    dtype: string
-  - name: adb
-    dtype: string
-  - name: idb
-    dtype: string
-  - name: isdb
-    dtype: string
-  - name: ebrd
-    dtype: string
-  - name: total_usd_mn
-    dtype: string
-  - name: total_per_capita_usd_mn
-    dtype: string
-  - name: total_as_percentage_of_gdp
-    dtype: string
-  - name: population
-    dtype: string
-  - name: gdp_per_capita
-    dtype: string
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 36358
-    num_examples: 216
-  - name: test
-    num_bytes: 9211
-    num_examples: 55
-  download_size: 28555
-  dataset_size: 45569
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 216
+    - name: test
+      num_examples: 54
 ---
+
+# Compilation of International Financial Institution and Economic Data
+
+**Publisher:** HDX · **Source:** [HDX](https://data.humdata.org/dataset/compilation-of-international-financial-institution-and-economic-data) · **License:** `cc-by` · **Updated:** 2025-08-26
+
+---
+
+## Abstract
+
+Compilation of international financial institution and economic data
+
+Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2025-08-26. Geographic scope: **AFG, ALB, AGO, ARG, ARM, BGD, BLR, BLZ, and 9 others**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Demographics and population |
+| **Unit of observation** | Country-level aggregates |
+| **Rows (total)** | 271 |
+| **Columns** | 17 (0 numeric, 17 categorical, 0 datetime) |
+| **Train split** | 216 rows |
+| **Test split** | 54 rows |
+| **Geographic scope** | AFG, ALB, AGO, ARG, ARM, BGD, BLR, BLZ, and 9 others |
+| **Publisher** | HDX |
+| **HDX last updated** | 2025-08-26 |
+
+---
+
+## Variables
+
+**Geographic** — `country_code` ( AFG,  ROU,  PLW), `country` ( Falkland Islands (Malvinas),  Montserrat,  Anguilla), `population`.
+
+**Demographic** — `total_as_percentage_of_gdp`.
+
+**Outcome / Measurement** — `total_usd_mn`, `total_per_capita_usd_mn`, `gdp_per_capita`.
+
+**Identifier / Metadata** — `idb` ( -   ,  990 ,  31 ), `esa_source`, `esa_processed`.
+
+**Other** — `world_bank` ( -   ,  25 ,  3 ), `ifc` ( -   ,  10 ,  15 ), `imf` ( -   ,  29 ,  14 ), `afdb` ( -   ,  14 ,  2 ), `adb` ( -   ,  20 ,  250 ) and 2 others.
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-compilation-of-international-financial-institution-and-economic-data")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
+
+print(train.shape)
+train.head()
+```
+
+---
+
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `country_code` | object | 1.8% |  AFG,  ROU,  PLW |
+| `country` | object | 1.1% |  Falkland Islands (Malvinas),  Montserrat,  Anguilla |
+| `world_bank` | object | 1.5% |  -   ,  25 ,  3  |
+| `ifc` | object | 1.5% |  -   ,  10 ,  15  |
+| `imf` | object | 1.5% |  -   ,  29 ,  14  |
+| `afdb` | object | 1.5% |  -   ,  14 ,  2  |
+| `adb` | object | 1.5% |  -   ,  20 ,  250  |
+| `idb` | object | 1.5% |  -   ,  990 ,  31  |
+| `isdb` | object | 1.5% |  -   ,  20 ,  9  |
+| `ebrd` | object | 1.5% |  -   ,  63 ,  49  |
+| `total_usd_mn` | object | 1.5% |  |
+| `total_per_capita_usd_mn` | object | 2.2% |  |
+| `total_as_percentage_of_gdp` | object | 29.5% |  |
+| `population` | object | 2.2% |  |
+| `gdp_per_capita` | object | 8.1% |  |
+| `esa_source` | object | 0.0% |  |
+| `esa_processed` | object | 0.0% |  |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+_No numeric columns._
+
+---
+
+## 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`. 134 exact duplicate rows were removed. 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 HDX 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: `total_as_percentage_of_gdp`.
+- This dataset spans 17 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
+- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/compilation-of-international-financial-institution-and-economic-data) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_compilation_of_international_financial_institution_and_economic_data,
+  title     = {Compilation of International Financial Institution and Economic Data},
+  author    = {HDX},
+  year      = {2025},
+  url       = {https://data.humdata.org/dataset/compilation-of-international-financial-institution-and-economic-data},
+  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.*