How do you handle missing data in machine learning projects?

Missing data handling strategy depends on missingness pattern and downstream model requirements.\n\n• Analysis: Identify MCAR, MAR, or MNAR patterns\n• Simple methods: Mean/median imputation, forward fill, deletion\n• Advanced: KNN imputation, iterative imputation, model-based methods\n• Indicator variables: Create missingness flags for informative patterns\n\nExample: For customer data with missing income, use KNN imputation based on age and education. Create binary indicator for missingness. Validate imputation quality using cross-validation and compare model performance.