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What techniques can be used to handle missing data?

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There are plenty of alternatives to handle missing data, although none of them is perfect or fits all cases. Some of them are:

  • Dropping incomplete rows: Simplest of all, it can be used if the amount of missing data is small and seemingly random.
  • Dropping variables: This can be used if the proportion of missing data in a feature is too big and the feature is of little significance to the analysis. In general, it should be avoided, as it usually throws away too much information.
  • Considering “not available” (NA) to be a value: Sometimes missing information is information in itself. Depending on the problem domain, missing values are sometimes non-random: Instead, they’re a byproduct of some underlying pattern.
  • Value imputation: This is the process of estimating the value of a missing field given other information from the sample. There are various viable kinds of imputation. Some examples are mean/mode/median imputation, KNN, regression models, and multiple imputations.

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