(Code) Iterative Imputer | MICE Imputer in Python | Machine Learning

#mice #python #iterative
In this tutorial, we'll look at Iterative Imputer from sklearn to implement Multivariate Imputation By Chained Equations (MICE) algorithm, a technique by which we can effortlessly impute missing values in a dataset by looking at data from other columns and trying to estimate the best prediction for each missing value.

Machine Learning models can't inherently work with missing data, and hence it becomes imperative to learn how to properly decide between different kinds of imputation techniques to achieve the best possible model for our use case.

I've uploaded all the relevant code and datasets used here (and all other tutorials for that matter) on my github page which is accessible here:

Link:

https://github.com/rachittoshniwal/machineLearning

Some useful resources that might be helpful for further reading:

https://cran.r-project.org/web/packages/mice/mice.pdf

https://stefvanbuuren.name/fimd/sec-MCAR.html

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/

https://towardsdatascience.com/all-about-missing-data-handling-b94b8b5d2184

https://towardsdatascience.com/how-to-handle-missing-data-8646b18db0d4

https://towardsdatascience.com/uncovering-missing-not-at-random-data-8d2cd3eda31a

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