📊 Data Analysis — Data Cleaning Playbook Demo

• Deduplication Engine — Exact-match, fuzzy-match, and time-window dedup strategies
• Standardization — Date formats, addresses, names, phone numbers, categorical encoding
• Outlier Detection — IQR, z-score, Modified Z-Score (MAD), and isolation-based methods
• Missing Value Strategies — Pattern analysis, imputation hierarchy, and indicator generation
• Validation Rule Engine — Declarative YAML rules for automated data quality checks

pip install -r requirements.txt
python -m src.cleaners.main

src/cleaners/
├── __init__.py            # Package exports
├── main.py                # Demo runner
├── deduplication.py       # Exact and fuzzy deduplication
├── standardization.py     # Date/name/address standardizers
├── outliers.py            # IQR, z-score, MAD outlier detection
├── missing_values.py      # Missing value analysis and imputation
├── validation_engine.py   # YAML-driven rule validation
rules/
├── data_quality_rules.yaml  # Example validation ruleset
├── common_patterns.yaml     # Regex patterns for data standardization
examples/
├── messy_customers.csv      # Sample messy data for testing
├── cleaning_pipeline.py     # End-to-end cleaning example
guides/
├── deduplication_strategies.md   # When to use which dedup approach
├── outlier_detection_guide.md    # Choosing the right detection method
├── missing_data_playbook.md      # Decision framework for missing values