Feature selection: A comprehensive list of strategies

Of course, the simplest strategy is to use your intuition. Sometimes it's obvious that some columns will not be used in any form in the final model (columns such as "ID", "FirstName", "LastName" etc). If you know that a particular column will not be used, feel free to drop it upfront. In our data, none of the columns stand out as such, so I'm not removing any in this step. Having missing values is not acceptable in machine learning, so people apply different strategies to clean up missing data (e.g., imputation).