In this post, you will learn about how to use Python’s Sklearn SimpleImputer for imputing/replacing numerical and categorical missing data using different strategies. In one of the related articles posted sometime back, the usage of fillna method of Pandas DataFrame is discussed. Here is the link, Replace missing values with mean, median and mode. Handling missing values is a key part of data preprocessing and hence, it is of utmost importance for data scientists/machine learning engineers to learn different techniques in relation imputing / replacing numerical or categorical missing values with appropriate value based on appropriate strategies.

The following topics will be covered in this post:

• SimpleImputer explained with Python code example
• SimpleImputer for imputing numerical missing data
• SimpleImputer for imputing categorical missing data

#tutorial #python #ai #artificial intelligence #sklearn #simpleimputer