Here we move through Feature Scaling/Variable Scaling.

What is feature scaling?

Feature scaling is a method used to normalize independent variables in the dataset. And this method is for continuos variables.

Why Feature Scaling?

Example : We have variables like below in a dataset
Age : From 30 to 80
Salary : From 30000 to 100000
If we use the same variables to be fed into the ML Model, then ML Algorithm tends to consider greater values as higher and smaller values as lower, regardless of unit of the values. To avoid this we go for feature scaling

How to do Feature Scaling

We do feature scaling on variables to make fit those variables( those are different in units) into particular range. It is done on continuous variable.

Methods of Feature Scaling

• Normalization
• Standardization

  Normalization

  Normalization is transforming the values into the range from 0 to 1. And below is the formula..
  

  Example for Normalization

  ###Calulate Min Value of Variable cyl

  Calculate Max Value of Variable cyl

  

  Calculate Min Value of Variable hp

  

  Calculate Max Value of Variable hp

  

  Calculate Normalized Value of Variable Cyl

  

  Caluclate Normalized Value of Variable hp

  

Here we end for now..

Will catch up with Standardization in the next post!!!