Python Feature Scaling in SciKit-Learn (Normalization vs Standardization)

Ғылым және технология

Today we take a look at how we can apply feature scaling to data sets within scikit-learn in python. This is useful when applying Normalization or standardization to data which allows for machine learning models to perform better.
Dataset is available on my Github
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Пікірлер: 17

  • @user-rw3vn5om3t
    @user-rw3vn5om3t4 ай бұрын

    underrated channel great video

  • @RyanNolanData

    @RyanNolanData

    4 ай бұрын

    Thanks

  • @v.jananayagan3284
    @v.jananayagan3284Ай бұрын

    you teach very well than other channels but i don't know why pepoles are not spend time on your channel really helpfull man

  • @RyanNolanData

    @RyanNolanData

    Ай бұрын

    Thanks it’ll happen over time

  • @sandeep-kc9hs
    @sandeep-kc9hs26 күн бұрын

    learned a lot from this. excellent teaching🙌

  • @qaisshefa4846
    @qaisshefa484623 күн бұрын

    Thanks so much

  • @sara-sx7gm
    @sara-sx7gmАй бұрын

    Helpful . Thank you so much

  • @RyanNolanData

    @RyanNolanData

    Ай бұрын

    No problem

  • @Welcomereddy
    @Welcomereddy11 ай бұрын

    Excellent brother !

  • @RyanNolanData

    @RyanNolanData

    11 ай бұрын

    Thank you!

  • @onurbltc
    @onurbltc11 ай бұрын

    Great video!

  • @RyanNolanData

    @RyanNolanData

    11 ай бұрын

    Thanks!

  • @lancerkind
    @lancerkind4 ай бұрын

    Very good video! I learned a lot. If I was to ask for more, it would be to fill in WHY normalize or standardized. You mention some about “getting your numbers in order.” Add to that there are reasons for visualization tools, comparison analysis, and whatever else. I have some ideas why, but I’m guessing as a Pandas user you have encountered many more. Thank you for sharing.

  • @RyanNolanData

    @RyanNolanData

    4 ай бұрын

    No problem and I may make a statistics course video in the future. Just waiting on my job to apply more skills

  • @lilikoimahalo
    @lilikoimahalo7 ай бұрын

    Could you also explain how the choice of feature_range affects the output processing please? Trying to understand in which case it should be (0,5) and when it should be (0,10), and how you then interpret the output, for example? Also, I am wondering: you are applying scalers to the whole dataset, but what if you have a regression type task (predicting an actual number)? If you apply scalers to all columns then your targets also change

  • @rishikeshjadhav4774
    @rishikeshjadhav477420 күн бұрын

    can you please post the jupyter notebook containing code , it will be very healpful

  • @RyanNolanData

    @RyanNolanData

    20 күн бұрын

    Will be on my website soon, I’m moving the code from the vids into articles

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