1.

How are missing values handled in Big Data?

Answer»

Missing values refer to the values that are not present for a particular COLUMN. If we do not take CARE of the missing values, it may lead to erroneous data and in turn incorrect results. So before processing the Big Data, we are required to properly treat the missing values so that we get the correct sample. There are various ways to handle missing values.

We can either drop the data or decide to replace them with the data imputation.

If the number of missing values is SMALL, then the general practice is to leave it. If the number of cases is more then the data imputation is done.

There are certain techniques in statistics to estimate the so-called missing values:

  1. Regression
  2. Maximum LIKELIHOOD Estimation,
  3. Listwise/pairwise Deletion
  4. Multiple data imputation etc.


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