When in the data preprocessing phase, how should one treat missing/null values?Remove the rows with missing values.Fill Missing values with the most common value in the column.Remove the whole column if it has more than 2% of the total size of the dataset as missing values.
Question
When in the data preprocessing phase, how should one treat missing/null values?Remove the rows with missing values.Fill Missing values with the most common value in the column.Remove the whole column if it has more than 2% of the total size of the dataset as missing values.
Solution
When dealing with missing or null values in the data preprocessing phase, there are several strategies that you can use:
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Remove the rows with missing values: This is the simplest approach and can be effective if the number of missing values is relatively small and randomly distributed. However, it can lead to loss of valuable information if the number of missing values is large.
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Fill Missing values with the most common value in the column: This is known as imputation and can be a good strategy if the data is categorical or if the missing values are likely to be similar to the most common values. However, it can introduce bias if the missing values are not randomly distributed.
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Remove the whole column if it has more than 2% of the total size of the dataset as missing values: This can be a good strategy if the column with missing values is not critical for your analysis or prediction. However, it can also lead to loss of valuable information and should be used with caution.
Remember, the best strategy depends on the nature of your data and the specific requirements of your analysis or prediction task. It's always a good idea to experiment with different strategies and see which one works best for your specific case.
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