PCA Analysis in Python Explained (Scikit - Learn)

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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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As a full-time data analyst/scientist at a fintech company specializing in combating fraud within underwriting and risk, I've transitioned from my background in Electrical Engineering to pursue my true passion: data. In this dynamic field, I've discovered a profound interest in leveraging data analytics to address complex challenges in the financial sector.

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RyanAndMattDataScience

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?

henry-oi

great video, thanks for explain clearly

alexhernandezherrera

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)"

bommijn

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 .

Divy

It's kinda difficult to understand the data, when you're not from a country like America, Canada or Japan where baseball is a common sport.

chsa-fpaqua

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