Imputation Techniques

This is a Jupyter notebook which contains examples of various Imputation techniques that can be used to fill the missing Data.
It contains brief explanation and example SimpleImputer, IterativeImputer and KNNImputer.
Please leave a like if you like my work and please share your valuable comments in terms of what can be improved.


This was insightful! :star_struck: But, it was lacking Visual representation, try to add more images/dataframe after imputation to show what is happening visually.

Sure, Thanks you so much for the suggestion

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I am getting a ‘Kernel is dead, it will automatically restart’ when I was trying to transform values of KNNImputer. What can be the possible way to solve this.

It could be due to many reasons, are you using a large dataset? Maybe you are getting out of memory. Try using colab and see if it works.

I ran it using binder and yes the data set is quite large 150K rows of data

I would recommend you to use a sample of the data first, say 20 or 30% of the data.
Also you might want to use colab, cause it gives a bit more computing power than binder.