How to impute the missing data with the most frequent value

DoomyDoomy Member Posts: 2 Contributor I
edited December 2021 in Help
I have a dataset containing categorical and numerical data and I would like to know how I can impute the missing data with the frequent value.
For example
ID         Feature1      Feature 2        Feature 3 
_______________________________________
123      core i7          Windows         33844690
334       IOS              phone             99983648

Note that the missing values are huge but I can't drop the column.
Is there an operator I can use for nominal data to be replaced with the mode? and the numerical data with average or max? 

Best Answer

  • mschmitzmschmitz Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 3,233 RM Data Scientist
    Solution Accepted
    Hi,
    you can use Replace Missing Values. Average actually takes the mode for nominal columns.

    Cheers,
    Martin
    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
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