I handle missing values by first looking at the percentage of the missing values, if the percentage is large and the feature is also not that useful it's better to drop it. Unless fill the missing values with the mean value in case of numeric column and for categorical column if we can see manually the relationship between the rows that have missing values and other rows then the values of that rows can be filled. Or we can fill 'Not available' also