Question

A data scientist is preparing a housing price dataset for a regression model. During EDA, they find that about 3% of the 'lot_area' values are extreme outliers caused by legitimate large commercial lots, not data-entry errors. The dataset is relatively small (2,000 rows), and dropping these rows would lose meaningful information. The scientist wants to reduce the influence of these extreme values on model training while retaining every observation. Which approach best meets this goal?

Accepted Answer

Winsorize (cap) the 'lot_area' values at a chosen upper percentile such as the 99th percentile

Answer

Delete all rows where 'lot_area' falls outside 1.5 * IQR from the quartiles

Answer

Replace the outlier 'lot_area' values with the column mean

Answer

Drop the 'lot_area' feature entirely since it contains extreme values