Data preparation : Scaling

Scaling is very important operation for machine learning . improper scaling creates bias in model. we don't want to have is data column that have very lager value range for example we have people age and salary we see that salary of a person is going to be much grater then the age hence it will create bias in model.

There are several methods used for scaling---

most common ones are--

1. Z score--

taking z score usually means normalizing the values with mean=0, standard deviation=1

2. min max -if distribution is far from normal then min max method is used

remember deal with missing values and errors and outliers before scaling because they create bias.

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