Add README.md

README.md

@@ -1,46 +1,166 @@
 ---
+annotations_creators:
+- no-annotation
+language_creators:
+- found
+language:
+- en
+license: other
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- tabular-classification
+- tabular-regression
+- other
+task_ids: []
+tags:
+- africa
+- humanitarian
+- hdx
+- electric-sheep-africa
+- development
+- education
+- health
+- indicators
+- mortality
+- nutrition
+- poverty
+- socioeconomics
+- sen
+pretty_name: "Senegal Multidimensional Poverty Index"
 dataset_info:
-  features:
-  - name: country_iso3
-    dtype: string
-  - name: admin_1_pcode
-    dtype: string
-  - name: admin_1_name
-    dtype: string
-  - name: mpi
-    dtype: float64
-  - name: headcount_ratio
-    dtype: float64
-  - name: intensity_of_deprivation
-    dtype: float64
-  - name: vulnerable_to_poverty
-    dtype: float64
-  - name: in_severe_poverty
-    dtype: float64
-  - name: survey
-    dtype: string
-  - name: start_date
-    dtype: timestamp[ns, tz=UTC]
-  - name: end_date
-    dtype: timestamp[ns, tz=UTC]
-  - name: esa_source
-    dtype: string
-  - name: esa_processed
-    dtype: string
   splits:
-  - name: train
-    num_bytes: 1327
-    num_examples: 12
-  - name: test
-    num_bytes: 320
-    num_examples: 3
-  download_size: 12502
-  dataset_size: 1647
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+    - name: train
+      num_examples: 12
+    - name: test
+      num_examples: 3
 ---
+
+# Senegal Multidimensional Poverty Index
+
+**Publisher:** Oxford Poverty & Human Development Initiative · **Source:** [HDX](https://data.humdata.org/dataset/senegal-mpi) · **License:** `other-pd-nr` · **Updated:** 2026-03-05
+
+---
+
+## Abstract
+
+The global Multidimensional Poverty Index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the acute deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. Critically, the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS), the Multi-Indicator Cluster Surveys (MICS) and in some cases, national surveys.
+
+The subnational multidimensional poverty data from the [data tables](https://ophi.org.uk/global-mpi-archive) are published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. For the details of the global MPI methodology, please see the latest Methodological Notes [found here](https://ophi.org.uk/publications-table?title=&field_authors_value=&field_publication_type_target_id=11&publication_year_filter=All&field_keywords_value=&field_country_target_id=All&field_region_target_id=All).
+
+Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-03-05. Geographic scope: **SEN**.
+
+*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*
+
+---
+
+## Dataset Characteristics
+
+| | |
+|---|---|
+| **Domain** | Public health |
+| **Unit of observation** | Country-level aggregates |
+| **Rows (total)** | 15 |
+| **Columns** | 13 (5 numeric, 6 categorical, 0 datetime) |
+| **Train split** | 12 rows |
+| **Test split** | 3 rows |
+| **Geographic scope** | SEN |
+| **Publisher** | Oxford Poverty & Human Development Initiative |
+| **HDX last updated** | 2026-03-05 |
+
+---
+
+## Variables
+
+**Geographic** — `country_iso3` (SEN), `admin_1_pcode` (SN01, SN02, SN03), `admin_1_name` (Dakar, Diourbel, Fatick), `intensity_of_deprivation` (range 41.2301–57.3021), `vulnerable_to_poverty` (range 9.0108–33.1552) and 2 others.
+
+**Temporal** — `start_date`, `end_date`.
+
+**Outcome / Measurement** — `headcount_ratio` (range 12.9108–85.7648).
+
+**Identifier / Metadata** — `esa_source` (HDX), `esa_processed` (2026-04-04).
+
+**Other** — `mpi` (range 0.0548–0.4915).
+
+---
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+ds    = load_dataset("electricsheepafrica/africa-senegal-mpi")
+train = ds["train"].to_pandas()
+test  = ds["test"].to_pandas()
+
+print(train.shape)
+train.head()
+```
+
+---
+
+## Schema
+
+| Column | Type | Null % | Range / Sample Values |
+|---|---|---|---|
+| `country_iso3` | object | 0.0% | SEN |
+| `admin_1_pcode` | object | 6.7% | SN01, SN02, SN03 |
+| `admin_1_name` | object | 6.7% | Dakar, Diourbel, Fatick |
+| `mpi` | float64 | 0.0% | 0.0548 – 0.4915 (mean 0.2731) |
+| `headcount_ratio` | float64 | 0.0% | 12.9108 – 85.7648 (mean 53.4142) |
+| `intensity_of_deprivation` | float64 | 0.0% | 41.2301 – 57.3021 (mean 49.6203) |
+| `vulnerable_to_poverty` | float64 | 0.0% | 9.0108 – 33.1552 (mean 20.3312) |
+| `in_severe_poverty` | float64 | 0.0% | 3.4639 – 63.571 (mean 28.4148) |
+| `survey` | object | 0.0% | DHS |
+| `start_date` | datetime64[ns, UTC] | 0.0% |  |
+| `end_date` | datetime64[ns, UTC] | 0.0% |  |
+| `esa_source` | object | 0.0% | HDX |
+| `esa_processed` | object | 0.0% | 2026-04-04 |
+
+---
+
+## Numeric Summary
+
+| Column | Min | Max | Mean | Median |
+|---|---|---|---|---|
+| `mpi` | 0.0548 | 0.4915 | 0.2731 | 0.2676 |
+| `headcount_ratio` | 12.9108 | 85.7648 | 53.4142 | 54.3119 |
+| `intensity_of_deprivation` | 41.2301 | 57.3021 | 49.6203 | 49.6238 |
+| `vulnerable_to_poverty` | 9.0108 | 33.1552 | 20.3312 | 20.9365 |
+| `in_severe_poverty` | 3.4639 | 63.571 | 28.4148 | 26.7985 |
+
+---
+
+## 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`. 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 Oxford Poverty & Human Development Initiative 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/senegal-mpi) for the publisher's own methodology notes and caveats.
+
+---
+
+## Citation
+
+```bibtex
+@dataset{hdx_africa_senegal_mpi,
+  title     = {Senegal Multidimensional Poverty Index},
+  author    = {Oxford Poverty & Human Development Initiative},
+  year      = {2026},
+  url       = {https://data.humdata.org/dataset/senegal-mpi},
+  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.*