There are various options for dealing with missing data:
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Rows with missing data should be deleted.
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Imputation of the mean, median, and mode
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Creating a one-of-a-kind value
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Predicting the values that are lacking
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Using a missing value-supporting method, such as random forests
Delete rows with missing data is the ideal technique since it assures that no bias or variance is added or removed, resulting in a robust and accurate model. This is only suggested if there is a large amount of data to begin with and a low percentage of missing values.