Question - How can we handle missing data?
Answer -
To be able to handle missing data, we first need to know the percentage of data missing in a particular column so that we can choose an appropriate strategy to handle the situation.
For example, if in a column the majority of the data is missing, then dropping the column is the best option, unless we have some means to make educated guesses about the missing values. However, if the amount of missing data is low, then we have several strategies to fill them up.
One way would be to fill them all up with a default value or a value that has the highest frequency in that column, such as 0 or 1, etc. This may be useful if the majority of the data in that column contains these values.
Another way is to fill up the missing values in the column with the mean of all the values in that column. This technique is usually preferred as the missing values have a higher chance of being closer to the mean than to the mode.
Finally, if we have a huge dataset and a few rows have values missing in some columns, then the easiest and fastest way is to drop those columns. Since the dataset is large, dropping a few columns should not be a problem anyway.