Normalization Methods for Numeric Data
#Statistics #Basics #Normalization
Normalization of data is critical for statistical analysis and feature engineering.
Minmax Normalization
This method is linear and straightforward.
Suppose we are analyzing series A, with elements $a_i$. We already know the min and max of the series, $a_{min}$ and $a_{max}$.
Now we would like to normalize the series to be within the range $[a_{min}', a_{max}']$. We simply solve the value of $a' _ i$ in $$ \frac{(a'i  a{min}')}{ ( a'{max}  a'{min} ) } = \frac{(a_i  a_{min})}{ ( a_{max}  a_{min} ) }, $$ where everything on the right hand side is known and $a_{min}'$ and $a_{max}'$ are chosen as the new min and max to be scaled to.
Zscore Normalization
Zscore normalization method normalizes the data using the standard deviation since standard deviation measures how are the data points devivate from the mean.
$$ a'_i = \frac{ (a_i  \bar A) }{ \sigma_A } $$
Decimal Scaling
Basically shifting the data with some powers of 10.
$$ a'_i = a_i/ 10^j $$
choose $j$ so that the new values are not larger than 1.
L Ma (2018). 'Normalization Methods for Numeric Data', Datumorphism, 11 April. Available at: https://datumorphism.leima.is/wiki/statistics/normalizationmethods/.
Table of Contents
Current Ref:

wiki/statistics/normalizationmethods.md