How to Clean Messy Datasets
Turn raw, chaotic data into analysis-ready gold
- Handle Missing Values: Impute with mean/median/mode, use KNN/regression, or drop if excessive.
- Remove Duplicates: Drop duplicate rows, ensure unique IDs for entities.
- Fix Inconsistencies: Standardize formats (dates, currencies, categories) & normalize text.
- Outlier Detection: Use Z-score, IQR, boxplots; cap, transform, or remove carefully.
- Normalize & Scale: Apply Min-Max scaling or Standardization for ML model readiness.
- Validate Data Types: Convert categorical → numerical where needed, parse date/time fields correctly.
📊 Data scientists spend up to 80% of their time cleaning and preparing data. Clean data = reliable insights + stronger models.
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