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2020-10-06
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2020-06-08
hello my name is Krishna and welcome to my youtube channel today in this particular video we'll be discussing the basic difference between normalization and standardization now guys I hope you have heard of this particular topic it is a very important topic in feature scaling which is an integral part of feature engineering so we should try to understand that why he should use feature scaling and what is the basic difference between normalization and standardization I'll also say that when to use this particular thing and when you should prefer for which algorithm you should prefer using this normalization or standardization technique you know and for which algorithm feature scaling suits you know it is not necessary that you need to apply the feature scaling for each and every algorithm okay so we will try to understand that I'll also show you the coding pattern coding how we can basically code how we can basically perform normalization and standardization with the help of a small data set that I have and I bounded it from tagging you know so I'll show you that particular example so make sure that you watch this particular video till the end now guys suppose if you have a use case and the most important thing for a use case is data so initially you'll be collecting the data and once you collect the data you will be having lot of features okay and those features will be including independent features and independent feature right with the help of the independent feature you will try to predict the dependent feature in supervised machine learning right so when you consider this features this features has two important properties one is unit the other one is magnitude I have collected some of the data with respect to a person and I have collected data like age weight height so all this particular information is collected now if I consider these two important properties that is unit and magnitude now if I consider the feature age the unit that is basically used to calculate age is number of years right from his date of birth like how many number of years has been happened right so apart from that the magnitude thing if I talk about magnitude that is basically the value suppose if I say 25 years the person age is 25 years so 25 over here is the magnitude and years right years is basically your unit so this is the basic thing and for each and every feature this will be there it will be either calculated with the use units it will be calculated with the help of units and magnitude now the main thing to understand is that if you are having many features it will definitely be getting computed by different different units and magnitudes right it need not be always same because if I take an example of height feature so in height feature it may be calculated using feed it may be calculated using hinges right so this unit and magnitude will always vary between all the features so it is very very necessary that for a machine learning algorithm the data that...
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