How do you handle missing data in a dataset?

Missing data handling is crucial for maintaining data quality and preventing biased analysis results.\n\n• Deletion: Remove rows/columns with missing values (if minimal impact)\n• Imputation: Fill with mean, median, mode, or forward/backward fill\n• Advanced: KNN imputation, regression imputation, multiple imputation\n• Indicator variables: Create flags to mark missing values\n• Domain-specific: Use business logic for appropriate handling\n\nExample: Customer age data with 10% missing values. Analyze missingness pattern (random vs systematic), use median imputation for numerical stability, create "age_missing" indicator variable, and validate impact on model performance through cross-validation.