Fill in the best CRISP-DM phase for each activity.   Definin…

Fill in the best CRISP-DM phase for each activity.   Defining the business success metric (e.g., reduce churn by 2 points) belongs to: Fixing missing values and parsing dates belongs to: Selecting features and training a regression model belongs to: Interpreting RMSE and whether it meets the business threshold belongs to:

You load a CSV and run the head(3) method to see the top few…

You load a CSV and run the head(3) method to see the top few rows of data. You see: df.head() #0  “$1,250.00” #1  “$980.50” #2  None #3  “$2,100.00” Which approach most directly converts revenue into a numeric column (float) with missing values preserved as NaN?