Handling Missing Data in Python: Simple Imputer in Python for Machine Learning

In this video, we'll dive into one of the fundamental steps in data preprocessing - handling missing values using the Simple Imputer module from sci-kit learn.
Missing data is a common issue in datasets, and knowing how to effectively handle it is crucial for building accurate and reliable machine learning models.
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[@RyanAndMattDataScience]

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[@RyanAndMattDataScience]

Wanted to leave a comment and mention most frequent can be used also for categorical data, mistake on my part when recording

[@WrongDescription]

Thanks a lot...you deserve a lot of views in this channel!

[@118bone]

Great videos! just a bit of feedback, please use dark mode, the white screen is blinding

[@starlettesupernova6653]

Hi ryan! Your videos are really useful and you make every concept much simpler. Thank you so much!

[@RyanAndMattDataScience]

No problem

[@pothalasrikanth8202]

I can't able to import the simple imputer it geting the error by packages

[@HediyeSılaÖzyurt]

I couldn't find that csv file on your github profile :'( could you help?

[@dgallas]

Great videos. Great playlist. Congratulations. Do you recommend any data visualisation playlists and videos, focused on matplotlib and seaborn? Thank you!

[@RyanAndMattDataScience]

I have a seaborn video and my other ML vids use both

[@hosseiniphysics8346]

tnx

[@RyanAndMattDataScience]

Np