Question

A data engineer is preparing a dataset in AWS Glue for a fraud-detection model. One numeric feature, transaction_amount, ranges from $1 to $500,000 and is heavily right-skewed, with most values clustered under $200 but a long tail of very large transactions. The team wants to reduce the disproportionate influence of the extreme values on a downstream logistic regression model while preserving the ordering of the data. Which transformation should the engineer apply to this feature?

Accepted Answer

Apply a log transformation to compress the range and reduce skew before scaling

Answer

Apply one-hot encoding to convert transaction_amount into categorical bins

Answer

Drop all transactions above the 95th percentile to remove the long tail

Answer

Replace the extreme values with the column mean using mean imputation