Which of the following best describes normalization's impact on data?

The process of normalization is aimed at scaling the features of a dataset to ensure that they fall within a specific range, often between 0 and 1, or to have a mean of 0 and a standard deviation of 1. This ensures that no particular feature dominates the others due to differing scales, allowing algorithms, particularly those sensitive to the magnitude of data (like gradient descent-based optimizations), to operate more effectively.

Normalization promotes data consistency, making it easier to compare and analyze features on a level playing field. For instance, in a dataset containing features like height in centimeters and weight in kilograms, normalization will adjust these values to a common scale, facilitating a more accurate interpretation and modeling process.

While normalization does not inherently deal with outliers (as option B suggests), and it does not involve changing categorical data into numerical data (as stated in option D), its main purpose is to standardize and achieve consistency in how data is presented across various attributes. Moreover, it does not necessarily increase complexity but rather aims to simplify the analysis process by standardizing values.