Example Concept: To assess the impact of missing data, first identify the percentage and pattern of missingness (e.g., Missing Completely at Random, Missing at Random, or Missing Not at Random). Use imputation techniques such as mean/mode imputation, K-nearest neighbors, or multiple imputation to fill in missing values. Train your model on both the original and imputed datasets, and compare performance metrics like accuracy, precision, recall, or RMSE to determine how missing data affects model outcomes.