What is a useful strategy to use when you are missing data?
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Data teams can use a number of strategies to handle missing data. On one hand, algorithms such as random forest and KNN are robust in dealing with missing values. On the other hand, you may have to deal with missing data on your own. The first common strategy for dealing with missing data is to delete the rows with missing values.
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Multiple imputation is another useful strategy for handling the missing data. In a multiple imputation, instead of substituting a single value for each missing data, the missing values are replaced with a set of plausible values which contain the natural variability and uncertainty of the right values.
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