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Scaling data in data science using Standardization and Normalization #shorts #datascience #machine
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#MachineLearning
#DataPreprocessing
#DataNormalization
#DataStandardization
#DataScaling
#ArtificialIntelligence
#AI
#DataScience
#shorts
Have you ever wondered what is the difference between normalized and standardized data? And which one should you use for your machine learning projects? Well, in this video, I will explain the basic definitions, advantages and disadvantages of these two scaling techniques, and how to choose the best one for your data.
Normalization is a way of transforming your data into a range between 0 and 1, or -1 and 1. It is useful when your data has different scales and you want to make it more homogeneous. However, normalization can be affected by outliers and it is not recommended when your data does not have a Gaussian distribution1.
Standardization is a way of transforming your data so that it has a mean of 0 and a standard deviation of 13. It is useful when your data has a Gaussian distribution and you want to compare different variables on the same scale. However, standardization can still be slightly affected by outliers and it is not recommended when your data has unknown or non-Gaussian distribution2.
So how do you decide which scaling technique to use? Well, there is no definitive answer, but you can try to observe the distribution of your data, apply different algorithms on raw, normalized and standardized data, and compare the results4. The best technique is the one that gives you the best performance and accuracy.
I hope this video was helpful and informative.
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#DataPreprocessing
#DataNormalization
#DataStandardization
#DataScaling
#ArtificialIntelligence
#AI
#DataScience
#shorts
Have you ever wondered what is the difference between normalized and standardized data? And which one should you use for your machine learning projects? Well, in this video, I will explain the basic definitions, advantages and disadvantages of these two scaling techniques, and how to choose the best one for your data.
Normalization is a way of transforming your data into a range between 0 and 1, or -1 and 1. It is useful when your data has different scales and you want to make it more homogeneous. However, normalization can be affected by outliers and it is not recommended when your data does not have a Gaussian distribution1.
Standardization is a way of transforming your data so that it has a mean of 0 and a standard deviation of 13. It is useful when your data has a Gaussian distribution and you want to compare different variables on the same scale. However, standardization can still be slightly affected by outliers and it is not recommended when your data has unknown or non-Gaussian distribution2.
So how do you decide which scaling technique to use? Well, there is no definitive answer, but you can try to observe the distribution of your data, apply different algorithms on raw, normalized and standardized data, and compare the results4. The best technique is the one that gives you the best performance and accuracy.
I hope this video was helpful and informative.
ABOUT OUR CHANNEL
Our channel is about Professional talks and free education. We cover lots of cool stuff such as data science, programming, software development, open source, data structures and algorithms, community building and tech talks
Check out our channel here:
Don’t forget to subscribe!
Our Founder Viram Bhadra's words
CHECK OUT OUR OTHER VIDEOS
Also Check out our website for more about us:
We are available on Whatsapp, Discord, Instagram, LinkedIn and YouTube. Join us on our journey by pressing on the links below:
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