Handling Missing Data In R: From Audit To Decision
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 677 MB
Audit, impute, and evaluate missing data in R with a practical, end‑to‑end workflow.
What you'll learn
Implement a complete missing data pipeline from audit to imputation decision using R.
Diagnose missingness mechanisms (MCAR/MAR/MNAR) and quantify risks with visualizations.
Compare imputation methods (mean/mode baselines vs MICE) using masking evaluation (MAE/RMSE).
Measure business impact of imputation choices on downstream model KPIs (AUC/accuracy).
Write defensible decision notes documenting assumptions, evidence, and sensitivity analysis.
Code:
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