Suppose if you have any use case, so the most important thing for the use case is data. Initially, you will be collecting the data so if you have collected the data that data have many features so those features may contain independent feature and dependent feature so with the help of the independent we will try to predict dependent feature in supervised machine learning.
Each feature is calculated with unit and magnitude so if you have many features so it will get computed with different units. so this unit and magnitude vary between different features. so it is very necessary that for the machine learning algorithm the data we provide that we should try to scale down the data with a particular scaling value.
For this type of problem, we use 2 main techniques.
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Normalization: Normalization helps you to scale down your feature between 0 to 1.
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Standardization: Standardization helps you to scale down your feature based on a standard normal distribution. Usually(Mean is 0 and Standard deviation is 1).
lets us discuss Normalization and Standardization. Please check the below mentioned links to know more about Normalization and Standardization.
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Normalization and Standardization Definetion. https://dslearningthoughts.blogspot.com/2021/07/normalization-and-standardization.html
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When to Use Normalization and Standardization. https://dslearningthoughts.blogspot.com/2021/09/normalization-and-standardization-train.html
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Simple Use case of Normalization. https://dslearningthoughts.blogspot.com/2021/09/normalization-and-standardization-use.html