Data Transformation (Scaling and Normalization)
Explore key data transformation methods including min-max scaling, z-score standardization, and robust scaling to handle outliers. This lesson helps you implement these techniques without external libraries, enhancing your ability to prepare data for stable and effective machine learning models.
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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:
Perform min-max scaling to transform data to a [0, 1] range.
Perform z-score standardization (zero mean, unit variance). ... ...