Example Concept: To assess the impact of missing data, you can perform a sensitivity analysis by creating multiple datasets with varying levels of missing data imputed using different techniques (e.g., mean imputation, k-nearest neighbors, or multiple imputation). Train and evaluate your model on each dataset using consistent metrics such as accuracy, precision, recall, or RMSE. Compare these metrics to understand how different imputation strategies and the extent of missing data influence model performance.