You are building a regression model to predict patient outcomes. In a dataset of 4,000 patients (all over age 65), 450 records have the age recorded as 0 while other features look reasonable. How should you handle these incorrect age values?

Replacing the incorrect age values of 0 with the mean or median age is the most appropriate strategy. Since the dataset specifies "all over age 65," an age of 0 is clearly an error, not a valid data point. Imputation with the mean or median preserves the records and allows the model to utilize other potentially valuable features from those 450 patients. Removing all 450 records would lead to a significant loss of data (over 10% of the dataset), potentially harming model performance. Dropping the entire age feature is too drastic, as age is likely a crucial predictor for patient outcomes. K-means clustering is not designed for imputing single numerical values in this manner; it's typically used for grouping data points or more complex imputation scenarios.