Example Concept: To assess the impact of missing data, first, quantify the amount and pattern of missingness in your dataset. Then, use imputation methods such as mean, median, or more advanced techniques like k-nearest neighbors or multiple imputation to fill in missing values. Train and evaluate your model on both the original dataset with missing values and the imputed dataset. Compare metrics such as accuracy, precision, recall, or RMSE to determine how missing data affects model predictions and whether imputation improves performance.