Comprehensive Guide to Data Normalization Techniques: From Min-Max Scaling to Box-Cox Transformation

Normalization techniques are essential in data preprocessing for machine learning tasks. They aim to scale numerical features to a similar range, which helps in better convergence of optimization algorithms and prevents certain features from dominating others. Here’s a tutorial on some common normalization techniques:

1. Min-Max Scaling (Normalization):

Min-Max scaling rescales the data set such that all features are exactly in the range [0, 1].

Formula:

2. Z-Score Normalization (Standardization):

Z-Score normalization transforms the data to have a mean of 0 and a standard deviation of 1.

Formula:

3. Robust Scaling:

Robust scaling scales features using statistics that are robust to outliers. It is similar to Min-Max scaling but uses the interquartile range (IQR) instead of the range.

Formula:

4. Unit Vector Scaling:

Unit vector scaling (or vector normalization) scales each data point such that the Euclidean norm (magnitude) of the…