To manage missing data, we must first determine the proportion of data that is missing in a certain column so that we may adopt an acceptable technique for dealing with the circumstance. If the bulk of the data in a column is missing, for example, removing the column is the best option unless we have some way of making reasonable estimates about the missing values. If the amount of missing data is little, however, we have numerous options for filling it in.
Filling them all with a default value or the value with the highest frequency in that column, such as 0 or 1, is one option. This might be handy if these values appear in the bulk of the data in that column.
Another option is to use the mean of all the values in the column to fill in the missing values in the column. This method is commonly used since missing values are more likely to be closer to the mean than to the mode.
Finally, if we have a large dataset and a few rows contain missing values in some columns, dropping those columns is the simplest and fastest solution. Dropping a few columns shouldn’t be a problem because the dataset is so vast.