26. Video: Removing Data - When Is It OK?

In the last video, you saw cases in which dropping rows or columns associated with missing values would not be a good idea. There are other cases in which dropping rows or columns associated with missing values would be okay.

A few instances in which dropping a row might be okay are:

1. Dropping missing data associated with mechanical failures.
2. The missing data is in a column that you are interested in predicting.

Other cases when you should consider dropping data that are not associated with missing data:

1. Dropping columns with no variability in the data.
2. Dropping data associated with information that you know is not correct.

In handling removing data, you should think more about why is this missing or why is this data incorrectly input to see if an alternative solution might be used than dropping the values.