Normalization & Standardization

Apart from missing or outlier treatment, Dimensionality reduction, one-hot encoding, Data Transformation is an important part of Data pre-processing stage. If done effectively, this leads to improved model performance.
There are many such techniques like - Log or power transformation, Winsorization or clipping, Unit Vector scaling, etc. Each of them have mathematical basis which makes it more popular in one area than other.
This video talks about two popular techniques of Data Transformation - Normalization & Standardization. Both of them can easily be implemented using popular tools like Python, R, etc.
For similar topics, visit - www.datarlabs....

[@Nazeerul_Hazard]

Very clear explanation. Thank you for this Anurag

[@dilinijayasinghe8134]

very good explanation. Thank you very much !!! I really wanted to understand these concepts.

[@raghav_birla]

Simplest explanation on UA-cam

[@datarlabs877]

Glad you liked it

[@dev0_018]

well explained, thanks 👍

[@darkredrose7683]

Would it make sense to do a kruskal-wallis significance test for scaled indices that have been scaled 0-1 with min-max? Thank you ❤ (for microbial ecology study)

[@freddyalyaqout6061]

This is Great!! Thank YOU !! ^.^

[@libyannbaby3461]

Great explanation
Please upload more videos on.machine learning topics

[@datarlabs877]

Will upload soon

[@debjanisarkar9136]

Great video Sir, can you please tell how to do the same in SPSS instead of Python. Regards

[@MohammadAli-cr9yi]

what is the benefit to scale down values between 0 to 1, can you please elaborate?

[@rodgerdodger88]

Where can I learn about the difference of (percent of total) and normalization. I find that I like my data to not stretch all the way to 0 or 1?