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5375cbf 390a4ee 5375cbf 0eb3e13 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | ## Dataset Description
**Purpose**: Demonstrate how data quality impacts analytics through the iconic Titanic dataset, featuring:
- **Original datasets** (with known age/class errors)
- **Corrected versions** (with reconciled passenger details)
- **Data quality annotations** (error flags, reconciliation sources)
**Homepage**: [Data Governance: Titanic Dataset and the Perils of Bad Data](https://www.databooth.com.au/posts/data-quality-titanic/)
**Repository**: `mjboothaus-titanic-databooth`
**Tasks**: `data-cleaning`, `error-detection`, `survival-prediction`
## Dataset Versions
| Version | Description | Key Features |
|---------|-------------|--------------|
| `original` | Unmodified datasets | Contains age discrepancies (e.g., Algernon Barkworth recorded as 80) |
| `corrected-v1` | Age-reconciled data | Matches Encyclopedia Titanica records |
| `annotated` | Error-flagged version | Contains `is_age_discrepancy` and `data_source` columns |
## Data Fields (Corrected Version)
| Column | Type | Description | Common Errors |
|--------|------|-------------|---------------|
| `name` | string | Passenger name | - |
| `age` | float | **Corrected age** at voyage | Original had 143+ age errors >2 years |
| `pclass` | int | Passenger class (1-3) | Class misassignments in original |
| `survived` | int | Survival status | - |
| `is_age_discrepancy` | bool | True if original age error >2 years | - |
| `data_source` | string | Reconciliation source (ET) | - |
## Usage Example
```
from datasets import load_dataset
# Compare original vs corrected data
original = load_dataset("mjboothaus/titanic-databooth", name="original")
corrected = load_dataset("mjboothaus/titanic-databooth", name="corrected-v1")
# Find corrected records
discrepancies = corrected.filter(lambda x: x["is_age_discrepancy"])
print(f"Fixed {len(discrepancies)} age errors")
```
<!-- TODO: Also st.connnector class? And "plain" class too for DuckDB? -->
## Key Data Quality Issues
1. **Age Discrepancies**
- Original error: 80yo survivor (actual age 47)
- 143+ passengers with >2 year age differences
- Systemic bias from death age vs voyage age confusion
2. **Class Misassignments**
- Documented cabin class errors
- Impacts fare/survival correlation analysis
## Reconciliation Process
1. **Source Alignment**: Cross-referenced with:
- Encyclopedia Titanica
- Titanic Facts Network
- Historical voyage manifests
2. **Validation Methods**:
- Age distribution analysis
- Survival rate by age cohort
- Source conflict resolution protocols
## Impact Analysis
| Metric | Original Data | Corrected Data |
|--------|---------------|----------------|
| Avg Age (Survivors) | 28.34 | 27.46 |
| Oldest Survivor | 80 (incorrect) | 64 (Mary Compton) |
| Class 1 Survival Rate | 62.96% | 63.01% (adjusted) |
## Suggested Use Cases
- **Data Quality Workshops**: Compare original/corrected versions
- **Governance Training**: Demonstrate error propagation
- **ML Robustness Tests**: Train models on both versions
## Citation
```
@dataset{titanic-databooth,
author = {Michael J. Booth},
title = {Titanic Data Quality Benchmark},
year = {2025},
publisher = {Hugging Face},
version = {1.0.0}
}
```
## Acknowledgements
- **Encyclopedia Titanica** for reference data
**Key Features to Highlight**:
- **Version Control**: Clear lineage between original/corrected data
- **Error Documentation**: Specific examples with historical context
- **Impact Metrics**: Quantifiable differences between datasets
- **Educational Focus**: Designed for data governance training
<!-- **TODO**: Include a Jupyter / Marimo / Streamlit notebook -->
**Code demonstrating**:
1. Age distribution comparisons
2. Survival rate analysis by data version
3. Simple ML model performance differences
## References:
Original "datacard" see https://huggingface.co/datasets/mjboothaus/titanic-databooth/resolve/main/titanic3info.txt
- [1] https://www.databooth.com.au/posts/data-quality-titanic/
- [2] https://mjboothaus.wordpress.com/2017/07/11/did-a-male-octogenarian-really-survive-the-sinking-of-the-rms-titanic-2/
---
license: apache-2.0
---
*Sponsored by: [DataBooth.com.au](https://www.databooth.com.au).* |