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

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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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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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Комментарии
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Hey guys I hope you enjoyed the video! If you did please subscribe to the channel!


*Both Datacamp and Stratascratch are affiliate links.

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

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

v.jananayagan
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Dude you just made the whole concept so easy to understand, i've been trying to understand exactly what was required of me for hours. Keep up the great work ❣❣❣❣❣❣

danieljuniormilazi
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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

lilikoimahalo
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learned a lot from this. excellent teaching🙌

sandeep-kchs
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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.

lancerkind