Standardization is a crucial process in data preprocessing that adjusts the features of different scales to have the same scale. This means converting the values to have a mean of zero and a standard deviation of one. It's particularly important for algorithms that rely on the scale of the data, such as logistic regression and K-means clustering, ensuring that all features contribute equally to the model performance.

Imagine you're comparing the weight and height of individuals where weight is in kilograms and height is in centimeters. If not standardized, the model might wrongly consider height more significant due to its larger range of values.

While standardizing features can be beneficial, especially for algorithms sensitive to the scale of data, it's not a universal requirement. The decision to standardize should be based on the nature of your data and the specific algorithms you plan to use.

In our case, we will standardize all numeric features in the Diamonds dataset for demonstrative purposes: carat, depth, table, x, y, z, and price.

Here’s how we select these numerical features: