Missing Data in Machine Learning Classification

1. How do you handle missing data in classification?
2. How much missing data is acceptable in machine learning?
3. How to handle missing categorical data in machine learning?
4. Does KNN work with missing values?

How do you handle missing data in classification?

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.

How much missing data is acceptable in machine learning?

How much data is missing? The overall percentage of data that is missing is important. Generally, if less than 5% of values are missing then it is acceptable to ignore them (REF).

How to handle missing categorical data in machine learning?

Replacing missing data with the most frequent values

When missing values is from categorical columns such as string or numerical then the missing values can be replaced with the most frequent category. If the number of missing values is very large then it can be replaced with a new category.

Does KNN work with missing values?

KNN is an algorithm that is useful for matching a point with its closest k neighbors in a multi-dimensional space. It can be used for data that are continuous, discrete, ordinal and categorical which makes it particularly useful for dealing with all kind of missing data.