PCA Analysis in Python Explained (Scikit - Learn)

Welcome to our comprehensive guide on Principal Component Analysis (PCA). In this video, we will go over what PCA is and why it's essential in data analysis and dimensionality reduction
and How to perform PCA step-by-step with practical examples in Python.
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[@user-iu5nz2gy6l]

Thanks- another great video. But i do have 2 questions? 1) how do i retrieve the column name of the component that has the most explained variance (for EDA purposes). 2) is PCA used for feature engineering? or will you have a video that talk about feature engineering later on?

[@alexhernandezherrera5198]

great video, thanks for explain clearly

[@RyanAndMattDataScience]

No problem

[@bommijn]

Hey, something i noticed. You copy the column names back into your X_train after scaling. Is it not easier to do "X_train = pd.DataFrame(ss.fit_transform(X_train), columns=X_train.columns)"

[@Divy91311]

Hey Ryan , really nice video! I was wondering if I could help you edit your videos and also make a highly engaging Thumbnail which will help your video to reach to a wider audience .

[@RyanAndMattDataScience]

Sorry have an editor and I make my own thumbnail. I appreciate you reaching out though

[@Divy91311]

@@RyanAndMattDataScience Sure bro no problem, if anyone in your contact is searching for an editor please refer me !!