---
license: mit
task_categories:
- tabular-regression
- tabular-classification
tags:
- nigeria
- education
- africa
- synthetic-data
- academic-outcomes
- synthetic
language:
- en
size_categories:
- 100K<n<1M
pretty_name: Nigeria Education - Academic Outcomes
data_type: synthetic
---

> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

# Nigeria - Academic Outcomes

## Dataset Description

Student academic outcomes including graduation, promotion, and retention data.

## Dataset Information

- **Country**: Nigeria
- **Dataset Name**: academic_outcomes
- **Total Records**: 100,000
- **Total Columns**: 9
- **File Size**: 1.62 MB
- **Format**: Parquet (full data), CSV (sample)
- **Generated**: 2025-10-21T23:16:04.156562

## Schema

| Column | Data Type | Description |
|--------|-----------|-------------|
| `outcome_id` | object | Outcome Id |
| `student_id` | object | Student Id |
| `academic_year` | object | Academic Year |
| `outcome` | object | Outcome |
| `final_gpa` | float64 | Final Gpa |
| `credits_earned` | float64 | Credits Earned |
| `honors` | object | Honors |
| `completion_date` | datetime64[ns] | Completion Date |
| `country` | object | Country |

## Sample Data

The dataset includes a 10,000-row sample in CSV format for quick exploration.

## Data Quality

- **Validation Status**: ✅ Passed
- **Missing Data**: ~2-5% (realistic pattern)
- **Data Type Enforcement**: Strict
- **Cross-Dataset Consistency**: Maintained

## Dependencies

- `cumulative_gpa`
- `attendance_logs`

## Usage Example

### Python (Pandas)
```python
import pandas as pd

# Load full dataset
df = pd.read_parquet('academic_outcomes_full.parquet')
print(f"Loaded {len(df):,} records")
print(df.head())

# Load sample
df_sample = pd.read_csv('academic_outcomes_sample.csv')
print(df_sample.describe())
```

### Python (Hugging Face Datasets)
```python
from datasets import load_dataset

# Load from Hugging Face
dataset = load_dataset('nigeria-education-academic_outcomes')
df = dataset['train'].to_pandas()
```

## Data Generation

This dataset was generated using statistical distributions based on:
- UNICEF education statistics for Nigeria
- World Bank development indicators
- Nigerian Ministry of Education data
- Realistic probability distributions

**Note**: This is synthetic data generated for research and testing purposes.

## Related Datasets

This dataset is part of the **Nigeria Education Datasets Collection** (45 datasets total).

Browse the full collection: [Nigeria Education Datasets](https://huggingface.co/collections/nigeria-education)

## Citation

```bibtex
@dataset{nigeria_academic_outcomes_2025,
  title={Nigeria Education Dataset: Academic Outcomes},
  author={[Your Name/Organization]},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/nigeria-education-academic_outcomes}
}
```

## License

[Specify your license]

## Contact

[Your contact information]

---

**Part of**: Nigeria Education Datasets Collection  
**Total Datasets**: 45  
**Country**: Nigeria 🇳🇬