README.md

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 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")

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

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.