Final answer:
The most practical action would be to remove the two feature columns that have 60% missing data each.
Step-by-step explanation:
The most practical action in this situation would be to remove the two feature columns that have 60% missing data each.
By removing these columns, you can still retain a significant portion of your dataset and avoid making assumptions or introducing bias by filling in missing data. Removing the rows with missing values would result in a significant loss of data and might not be ideal if the remaining dataset is too small for analysis.