Normalization is a data preparation technique that is frequently used in machine learning.
Data Normalization is a common practice in machine learning which consists of transforming numeric columns to a common scale. i.e. it transforms multi-scaled data to the same scale.
In machine learning, some feature values differ from others multiple times. The features with higher values will dominate the learning process. However, it does not mean those variables are more important to predict the outcome of the model. Let’s take a look at the below example, which will explain this more clearly.
After normalization, all variables have a similar influence on the model, improving the stability and performance of the learning algorithm.
Methods of Data Normalization –
1) Decimal Scaling
2) Min-Max Normalization
3) z-Score Normalization(zero-mean Normalization)