Machine learning feature engineering: Label encoding Vs One-Hot encoding (using Scikit-learn)

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In this tutorial, you will learn how to apply Label encoding & One-hot encoding using Scikit-learn and pandas. Encoding is a method to convert categorical variable into numerical variables, which is going to create better features for machine learning models, ready to learn in just than 10 minutes?

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Thank you very much! the video was clear, direct and informative :)

standjustice

thanks for tutorial ....i like the way u explained

ameerabdul

I was expecting to learn when and why use each one also the main differences. Anyway, thanks for the great class.

cristianofroes

I suppose it would have been useful to mention when to use each type of encoding and what are their pros and cons

havryliuk

Hi, thx for the tutorial, however the sklearn doc specifies that you should use LabelEncoder for target not for features: "This transformer should be used to encode target values, i.e. y, and not the input X."

TheAnbohan

airbnb.shape( ), it would be more easy to understand .

pkstock

Can someone explain on what basis the labels are generated? Like why did Staten Island get 3, Manhattan 2 etc?

sasidharansathiyamoorthy

Focus on indian students, you have good scope...

irfanfulari

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