How do I handle missing data in machine learning?

How do I handle missing data in machine learning?

Handling missing data is crucial in machine learning. You can use techniques like imputation, deletion, or using algorithms that support missing values.

What is imputation, and how do I apply it?

Imputation is the process of replacing missing values with substituted values. Common methods include mean, median, or mode imputation.

Are there any specific algorithms that can handle missing data?

Yes, some algorithms like decision trees and k-nearest neighbors can handle missing data without requiring imputation.

What are the risks of deleting rows with missing data?

Deleting rows can lead to loss of valuable information and may introduce bias if the missing data is not random.