Machine Learning in Python: Principal Component Analysis (PCA) for Handling High-Dimensional Data




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In this video, I will be showing you how to perform principal component analysis (PCA) in Python using the scikit-learn package. PCA represents a powerful learning approach that enables the analysis of high-dimensional data as well as reveal the contribution of descriptors in governing the distribution of data clusters. Particularly, we will be creating PCA scree plot, scores plot and loadings plot.

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Comment List

  • Data Professor
    January 9, 2021

    Anyone interested in a tutorial video on Bioinformatics (using Python)? Comments down below πŸ‘‡
    If you find value in this video, please give it a Like πŸ‘ and Subscribe ❀️for more videos on Data Science. πŸ˜ƒ

  • Data Professor
    January 9, 2021

    Thanks professor 😊

  • Data Professor
    January 9, 2021

    can we also make it to plot 2d scatter plots of different combinations instead of 3d with a for loop in plotly or we need to use matplotlib for it ? also what could be the usage of pca for bioinformatics ?

  • Data Professor
    January 9, 2021

    Incredible tutorial, congratulations! The 3d viz are awesome and help a lot to understand loadings x attributes relationship. I was wondering if isnt possible to access the scoring coeeficient matrix that is used internally by the Transform(X). Do you know how can I achieve that?

  • Data Professor
    January 9, 2021

    Hi sir, thank you very much for the video! I have experimental data set of time dependent signals 800(time)x49(signals) voltage values. I used PCA and reduced it to 800×2.How can I reduce further and extract information from these set for ML application and is there any other feature extraction method that you can advice for signal feature extraction ?

  • Data Professor
    January 9, 2021

    How do you transform your own dataset into sklearns format?

  • Data Professor
    January 9, 2021

    How could we know that the components need to be only 3 in the starting
    pca = PCA(n_components=3)

  • Data Professor
    January 9, 2021

    hi, why not use the PCA function directly?

    from sklearn.decomposition import PCA

    pca = PCA(n_components=3)

    pca.fit(X)

  • Data Professor
    January 9, 2021

    Interesting! I was just following an online course about PCAπŸ˜„

  • Data Professor
    January 9, 2021

    Wow I loved those plots 😍

  • Data Professor
    January 9, 2021

    Very informative! Thanks for this important lesson πŸ™‚ keep it up!

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