### What is Expectation-Maximization?

Use the iterative EM algorithm to impute missing observations using the “most likely” values based on the complete observations.

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Replace the missing values with the average of the observation’s k-nearest neighbors.

Replace the missing values with an arbitrary value located at the far end of the distribution of the feature, for example 999

Replace the missing values with the most frequently occurring value from the non-missing observations.

Mean Imputation, Mode Imputation, Extreme Value Imputation, Nearest Neighbor Imputation, Expectation Maximization

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