In today's lesson, we’ll examine an important preprocessing step for predictive modeling: normalizing features. Normalization adjusts the scale of feature data so that no single feature with a larger or smaller scale dominates the model. Our mission is to learn why normalization is necessary and to understand two primary methods of normalization, applying these techniques to the California Housing Dataset using Python.

Normalization addresses the issue of features having different ranges. Without scaling, features with larger value ranges could unfairly influence the results of our predictive model. In simple terms, if one feature has values ranging from 0 to 100 and another from 0 to 1, the first feature might dominate the model training process. As we work with features like house age and median income, normalizing helps ensure that each feature contributes to the model based on its importance, not merely its scale.

Standard scaling is a method that rescales the features so that they have a mean of zero and a standard deviation of one. This method calculates the z-score of each data point, which represents how many standard deviations a data point is from the mean. Let's apply standard scaling using Python:

Min-max scaling is another technique that scales the data so that all the feature values are in the range of 0 to 1. This scaling ensures that values closer to 0 are closer to the minimum value of the raw data, while values closer to 1 are closer to the maximum value of the raw data. Let's see how this is done in practice:

Normalization techniques can be applied to feature data individually through separate fit and transform processes or simultaneously using the fit_transform method, which is a convenient way to perform both steps in one call. This method is particularly useful during the initial model training phase when preparing your dataset:

Normalization ensures that all features are on a similar scale and range, which is crucial for the balanced contribution of each feature to the predictive model. In this lesson, we've explained two techniques of normalization—standardization and min-max scaling—and provided instances where each is suitable. We have transformed features using these methods and considered their impact on datasets. Now you'll be applying normalization techniques to datasets, reinforcing learning through hands-on exercises. These exercises will allow you to grasp how different normalization methods can affect your model's predictive power.