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.
- 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).
- 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.
Happy Studying 🐷!