Question

During exploratory data analysis on a Spark DataFrame of customer records, a data scientist computes summary statistics with df.describe() and notices that the numeric column 'account_region_code' has a standard deviation of 0.0 and identical min, max, and mean values across all 500,000 rows. What is the most appropriate action to take before model training?

Accepted Answer

Drop the column, because a feature with zero variance provides no discriminative information for the model

Answer

Apply StandardScaler to the column so its variance is normalized to 1.0 before training

Answer

Impute the column values with the median to introduce variability into the feature

Answer

Keep the column and apply one-hot encoding since it appears to be a categorical identifier