I will do missing value imputation simulation study using ml

If you need to amputate (introduce) missing data (MCAR, MAR, MNAR), perform masked imputation, and evaluate secondary parameter estimations to compare algorithm performance (kNN, MICE, MissForest/RF, XGBoost, rpart), please contact me for a customized offer.

Generic Offer:

I provide reliable missing value and outlier imputation using both machine learning and statistical methods, tailored to your data structure and research goals. My workflow is fully reproducible in R, with careful diagnostics before and after imputation.

Missing Value Imputation:

• kNN imputation
• Hot-deck imputation
• Regression-based imputation (linear models)
• Regression tree imputation (rpart)
• Iterative Robust Model-Based Imputation (IRMI)
• Fully multivariate, mixed-type data support (MICE)

Outlier Treatment (VIM & robust methods):

• Winsorization
• Boxplot / IQR-based detection
• Robust distance & multivariate outlier handling

ML-based options (when appropriate):

• Random Forest imputation
• XGBoost Imputation
• Predictive mean matching & hybrid approaches

You'll receive cleaned data, clear method justification, Visualization and optional R scripts for transparency and reuse.