Handling Missing Data with SimpleImputer - Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Missing data in machine learning is a type of data that contains "None" or "NaN" type of values. One should take care of the missing data while dealing with machine learning algorithms and training. Missing data can be filled using basic python programming, pandas library, and a sci-kit learn library named SimpleImputer. Handling missing values using the sci-kit learns library SimpleImputer is the easiest and most convenient method of all the other missing data handling methods.