How to handle missing data machine learning

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.

1. How do we deal with missing data in machine learning?
2. What is the best way to handle missing data?

How do we deal with missing data in machine learning?

One way of handling missing values is the deletion of the rows or columns having null values. If any columns have more than half of the values as null then you can drop the entire column. In the same way, rows can also be dropped if having one or more columns values as null.

What is the best way to handle missing data?

Mean, Median and Mode

This is one of the most common methods of imputing values when dealing with missing data. In cases where there are a small number of missing observations, data scientists can calculate the mean or median of the existing observations open_in_new.