When performing data normalization, what is the primary purpose?

Data normalization is a technique used to scale numerical data within a specified range, often between 0 and 1. This process helps to ensure that each feature contributes equally to the analysis or modeling process, preventing certain features from dominating others due to their larger scale. This is particularly important in algorithms such as k-nearest neighbors (KNN) or neural networks , which are sensitive to the scale of the data. Normalizing the data ensures that all features are treated equally, regardless of their original units or magnitudes. Why Other Options Are Wrong : A) Incorrect : Encoding categorical data is a process of converting non-numeric categories into numbers (e.g., using one-hot encoding or label encoding), not the goal of normalization. B) Incorrect : Eliminating missing or duplicate values is part of data cleaning , not normalization. D) Incorrect : Standardizing units of measurement is not part of normalization. This is usually handled separately during data cleaning or transformation. E) Incorrect : Identifying and removing outliers is part of the data cleaning process, not normalization. Outliers may affect normalization, but they are handled separately.