Describe the process of feature scaling and provide a scenario where standardization would be preferred over normalization and vice-versa.

Feature scaling is a preprocessing step applied to independent variables or features of data, primarily used in machine learning, to transform the range of data. This is essential because many machine learning algorithms, especially those using distance-based calculations such as K-Nearest Neighbors or gradient descent-based algorithms such as linear regression and neural networks, are sensitive to the scale of the input features. When features have vastly different scales (e.g., one ranging from 0 to 1 and another from 100 to 1000), the larger-scale features might dominate or skew the learning process if not scaled to the same range. Feature scaling techniques bring all features to a similar scale, ensuring that each feature contributes proportionally and equally during the learning process. There are two common types of feature scaling methods: Normalization and Standardization. Normalization, also known as min-max scaling, transforms data to fit within a specified range, often between 0 and 1. The formula for normalization is typically: x_scaled = (x - x_min) / (x_max - x_min), where x is the original feature value, x_min is the minimum value of the feature in the dataset, and x_max is the maximum value of t....