Data Transformation (Scaling and Normalization)

Scaling and normalization are important for building stable, performant models. In this lesson, we’ll learn how to implement standard techniques like min-max scaling and z-score standardization and extend them to robust scaling that can handle real-world outliers. Let’s get started.

Min-max scaling and z-score standardization

You’re given a dataset containing numerical features. Implement two custom scaling methods for a given dataset without using scikit-learn’s preprocessing module. Create functions that:

1. Perform min-max scaling to transform data to a [0, 1] range.

2. Perform z-score standardization (zero mean, unit variance). ...