One Punch Dan （一拳丹）

It is quite normal that there’s a lot of missing values here and there when you get a dataset in the real world. How to deal with missing values is a big topic. As far as I’m concerned, there’s no one optimal method that works for all situations, usually, it is case-by-case.  For me, most of the time I don’t want to throw away observations that contains missing values, unless the data is really large while the missing value percentage under certain features are quite high (e.g. >98%) and those features do not play an important role in the analysis. Otherwise I’ll try my best to impute all the missing values to retain important information in the data as much as possible. There’s a lot of missing value imputation methods out there, including statistical methods and machine learning methods. No matter what method we use, we want a data distribution (after imputation) that is not twisted, or in other words, is not statistically significantly different from the original distribution...