Scenario: You are tasked with cleansing a dataset containing customer information. How would you handle missing values in the "Age" column?

When handling missing values in the "Age" column, one approach is to impute the missing values based on other demographic information such as gender, location, or income. This method utilizes existing data patterns to estimate the missing values more accurately. Replacing missing values with the mean or median can skew the distribution, while removing rows may result in significant data loss. Flagging missing values for further investigation allows for manual review or additional data collection if necessary.