What are the standard ways of filling missing values in python?
To fill the missing values, you can do one of the following:
1) Compute the mean of the feature using the available values and then fill the missing values with the mean. If the values are discrete (categorical), then use the most frequent value (mode) to fill the missing ones.
2) Find the most similar example(s) to the one that has a missing value given that these examples have a value for the particular feature. Then use their mean/mode along the feature you’re interested in to fill the missing values.
I have a very limited dataset having variety of columns having missing values. I can not prune the rows having missing values as it will reduce the size drastically. Can anyone suggest, standard procedure for it ?