Using Column Transformer and Pipeline to handle data with missing values | Machine Learning

In this tutorial, we'll build upon what we learnt in Column Transformer (Part 1), but here we'll look at an example dataset which has missing values, and we'll figure out a way to apply pre-processing steps to such datasets using scikit-learn.

Using Column Transformer and Pipelines to do the data pre-processing helps us in more ways than one. First is the easy interpretability, second it helps to prevent data leakage and third it helps us in doing hyper parameter tuning with the help of GridSearchCV etc, among others.

In the tutorial, we'll be going through all the nitty-gritties of when, how, where to use them.

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/ma...

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