Normalization

Data science Day 8:

 

Data transformation is one of the critical steps in Data Mining.  Among many data transformation methods, normalization is a most frequently used technique. For example, we can use Z-score normalization to reduce possible noise in sound frequency.

We will introduce three common normalization method, Max-Min Normalization, Z-Score Normalization, Scale multiplication.

  • Max-Min Normalization
    x_normal= (x- min(x))/ (max(x)- min(x))
    it will scale all the data between 0 and 1. 
    Example: Chinese high schools use 150 point scale, USA high schools use 100 point scale and Russian high schools use 5 point scale.

 

norm1

  • Z-Score Normalization
    X_z-normal= (X- mean)/ sd
    It will transform the data in units relative to the standard deviation.
    Example: It is useful when comparing data sets with different units (cm and inch).

norm2

 

  • Scale multiplication

    Z_z-normal =X*10  or  Z_z-normal =X/10
    It will transform the data in scales of muliple of 10.
    Example: Some money transactions are too large, we will divide 1000 to make it viewer friendly.

norm3

Happy Studying 🐷!

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