Datasets:

electricsheepafrica
/

africa-synth-financial-inclusion-credit-access-patterns-africa-all

@@ -1,123 +1,143 @@
 ---
 license: cc-by-4.0
 task_categories:
 - tabular-classification
 - tabular-regression
-language:
-- en
-tags:
-- financial-inclusion
-- fintech
-- africa
-- synthetic-data
-- sub-saharan-africa
-- credit-access
-- synthetic
 size_categories:
 - 10K<n<100K
-data_type: synthetic
 ---
-> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.
-# Credit Access Patterns in Africa
-Synthetic dataset modeling credit access, approval, and repayment patterns across 15 Sub-Saharan African countries from 2018-2025.
-## Dataset Description
-This dataset simulates individual-level credit seeking behavior, application outcomes, and repayment patterns. It captures the spectrum of formal and informal credit sources, from commercial banks to family loans, and the barriers faced by those seeking credit.
-### Key Statistics
-| Metric | Value |
-|--------|-------|
-| Total Records | 15,000 |
-| Countries | 15 |
-| Time Period | 2018-2025 |
-| Credit Need Rate | ~40% |
-| Formal Credit Access | ~15% |
-| Approval Rate (formal) | ~45% |
-| Approval Rate (informal) | ~75% |
-| Avg Loan Amount | $500-2,000 USD |
-| Interest Rate Range | 5-120% APR |
-### Coverage by Scenario
-- `low_burden`: 4,000 records
-- `moderate_burden`: 5,000 records
-- `high_burden`: 6,000 records
-## Column Descriptions
-| Column | Type | Description |
-|--------|------|-------------|
-| country | string | One of 15 SSA countries |
-| year | int | Year (2018-2025) |
-| urban_rural | string | Urban or rural location |
-| gender | string | Male or female |
-| age | int | Age in years (18-70) |
-| age_group | string | Age bracket |
-| income_quintile | string | Income group (lowest to highest) |
-| education | string | Education level |
-| employment_status | string | Employment category |
-| monthly_income_usd | float | Monthly income in USD |
-| income_stability | int | Income stability score (1-10) |
-| credit_history | string | Credit history quality |
-| has_bank_account | int | Has bank account (0/1) |
-| credit_score | int | Credit score (0-850) |
-| credit_need | int | Needs credit (0/1) |
-| preferred_credit_source | string | Preferred lender type |
-| loan_amount_requested_usd | float | Requested loan amount |
-| collateral_available | string | Collateral type |
-| loan_purpose | string | Purpose of loan |
-| approved | int | Loan approved (0/1) |
-| loan_disbursed_usd | float | Disbursed loan amount |
-| interest_rate | float | Annual interest rate |
-| loan_term_months | int | Loan term in months |
-| monthly_payment_usd | float | Monthly payment |
-| repayment_status | string | Current repayment status |
-| rejection_reason | string | Reason for rejection |
-| scenario | string | Burden scenario label |
-## Usage Example
 ```python
-import pandas as pd
-# Load the combined dataset
-df = pd.read_csv("credit_access_combined.csv")
-# Analyze credit sources
-sources = df[df['credit_need'] == 1]['preferred_credit_source'].value_counts()
-# Predict loan approval
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.model_selection import train_test_split
-seekers = df[df['credit_need'] == 1].copy()
-features = ['age', 'credit_score', 'income_stability', 'has_bank_account', 'collateral_available']
-X = pd.get_dummies(seekers[features], drop_first=True)
-y = seekers['approved']
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
-model = RandomForestClassifier().fit(X_train, y_train)
-print(f"Accuracy: {model.score(X_test, y_test):.2f}")
 ```
-## Research Sources
-- World Bank Global Findex Database 2025
-- IMF Financial Access Survey 2024
-- FSD Kenya credit market research 2024
-- CGAP credit reporting research
 ## Citation
 ```bibtex
-@dataset{credit_access_africa_2025,
-  title={Credit Access Patterns in Africa},
-  year={2025},
-  note={Synthetic dataset based on World Bank Findex and IMF data}
 }
 ```

 ---
 license: cc-by-4.0
+language:
+- en
 task_categories:
 - tabular-classification
 - tabular-regression
+multilinguality: monolingual
 size_categories:
 - 10K<n<100K
+tags:
+- "africa"
+- "electric-sheep-africa"
+- "open-data"
+- "metadata-backed"
+- "economics-finance"
+- "csv"
+- "tabular"
+- "text"
+- "financial-inclusion"
+- "fintech"
+- "synthetic-data"
+- "sub-saharan-africa"
+- "credit-access"
+- "synthetic"
+- "financial"
+pretty_name: "Africa Synth Financial Inclusion Credit Access Patterns Africa All | Africa (Electric Sheep Africa metadata inventory)"
 ---
+# Africa Synth Financial Inclusion Credit Access Patterns Africa All | Africa (Electric Sheep Africa metadata inventory)
+**Size category:** `10K<n<100K` - **Formats:** `csv` - **Sector:** economics_finance - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)*
+![size](https://img.shields.io/badge/size-10K%3Cn%3C100K-blue)
+![sector](https://img.shields.io/badge/sector-economics_finance-green)
+![downloads](https://img.shields.io/badge/HF_downloads-78-orange)
+![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey)
+## TL;DR
+This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
+## What This Dataset Covers
+Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.
+Dataset context from the existing Hugging Face card: ⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference. Credit Access Patterns in Africa Synthetic dataset modeling credit access, approval, and repayment patterns across 15 Sub-Saharan African countries from 2018-2025. Dataset Description This dataset simulates individual-level credit seeking behavior, application outcomes, and repayment patterns. It captures the spectrum of… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-financial-inclusion-credit-access-patterns-africa-all.
+## Dataset Profile
+| Field | Value |
+|---|---|
+| Hugging Face repo | [`electricsheepafrica/africa-synth-financial-inclusion-credit-access-patterns-africa-all`](https://huggingface.co/datasets/electricsheepafrica/africa-synth-financial-inclusion-credit-access-patterns-africa-all) |
+| Sector | economics_finance |
+| Topic tags | financial-inclusion, fintech, synthetic-data, sub-saharan-africa, credit-access, synthetic |
+| Modalities | `tabular`, `text` |
+| Formats | `csv` |
+| Size category | `10K<n<100K` |
+| Countries | Africa-wide or source-defined African coverage |
+| ISO3 coverage | `not declared` |
+| Last modified on HF | `2026-04-14 22:53:30+00:00` |
+| Inventory snapshot | `2026-07-16T16:00:34Z` |
+## How To Read This Dataset
+- Start from the repository files and the dataset viewer when available.
+- Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
+- Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
+- Preserve missing values until you have a defensible imputation rule.
+## Usage
 ```python
+from datasets import load_dataset
+ds = load_dataset("electricsheepafrica/africa-synth-financial-inclusion-credit-access-patterns-africa-all")
+print(ds)
+split_name = next(iter(ds))
+table = ds[split_name]
+print(table.features)
+print(table[:3])
+```
+### Convert To Pandas When Tabular
+```python
+from datasets import Dataset
+first_split = ds[next(iter(ds))]
+if isinstance(first_split, Dataset):
+    df = first_split.to_pandas()
+    print(df.head())
 ```
+## Data Quality Notes
+- This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
+- Exact schema, row counts, and source files should be inspected in the repository data files.
+- Metadata gaps from the inventory: country, upstream_publisher.
+- Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.
+## Source And Provenance
+- **Source context:** Electric Sheep Africa metadata inventory
+- **Publisher/source attribution:** Public dataset metadata
+- **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
+- **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-synth-financial-inclusion-credit-access-patterns-africa-all](https://huggingface.co/datasets/electricsheepafrica/africa-synth-financial-inclusion-credit-access-patterns-africa-all)
+- **Inventory retrieved at:** `2026-07-16T16:00:34Z`
+## Suggested Analyses
+- Inspect schema and missingness before modeling.
+- Profile variables by geography, time, and subgroup columns where present.
+- Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
+- Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.
 ## Citation
 ```bibtex
+@misc{electric_sheep_africa_africa_synth_financial_inclusion_credit_access_patterns_africa_all_2026,
+  title        = {Africa Synth Financial Inclusion Credit Access Patterns Africa All | Africa (Electric Sheep Africa metadata inventory)},
+  author       = {Public dataset metadata},
+  year         = {2026},
+  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-financial-inclusion-credit-access-patterns-africa-all},
+  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
+  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-financial-inclusion-credit-access-patterns-africa-all}}
 }
 ```
+## License
+Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
+Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.
+## About Electric Sheep Africa
+Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.
+---
+Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.