Question

During exploratory data analysis of a housing dataset, a data scientist computes summary statistics on the 'lot_area' feature and finds that the mean (18,400) is far larger than the median (9,600), with a maximum value of 1,200,000. A histogram shows a long right tail. The scientist wants to reduce the impact of this heavy right skew before training a linear regression model, without discarding any rows. Which approach best addresses the skew while preserving all observations?

Accepted Answer

Apply a log transformation (e.g., log1p) to the 'lot_area' feature to compress the large values and reduce skew

Answer

Remove all rows where 'lot_area' exceeds the 99th percentile to eliminate the extreme values

Answer

Replace all 'lot_area' values with the median (9,600) to neutralize the effect of the outliers

Answer

Apply StandardScaler to 'lot_area' so the feature has zero mean and unit variance