I think it depends on context: what is the proportion of missing values compared to the size of the whole dataset? If the variable is numeric can I implement the mean or the median instead? For the categorical variable, can I remove them? Whatever the decision of the person who analyzes the most important is to justify why we take this decision. For example, the missing values corresponded to 3% of the dataset, as it was few compare to the large dataset (100000 rows, 10 variables), we can remove these data.
Dear doctor
I do hope the following words give a notable answer
"Missing values can be handled by deleting the rows or columns having null values. If columns have more than half of the rows as null then the entire column can be dropped. The rows which are having one or more columns values as null can also be dropped."
Sincere Greetings and Regards
Dr.Sundus Fadhil Hantoosh
The analyst has 2 choices: delete the rows of missing values or fill the missing values with the (MEAN) value of the specific column, it depends on the objective of the study and the size of the dataset.
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