Data Analytics – Adaptive Reading Practice Test

The main purpose of normalization in data preprocessing is to scale data into a standard range, which is crucial in preparing data for analysis, especially for machine learning algorithms. Many algorithms, such as k-nearest neighbors or gradient descent-based methods, are sensitive to the scale of the data. If features have different ranges, those with larger ranges can disproportionately influence the results. Normalization helps to prevent this by transforming the data to a common scale, typically between 0 and 1 or -1 and 1, which can enhance the performance and convergence speed of the algorithm by ensuring that each feature contributes equally.

This process does not distort differences in the ranges of values; rather, it standardizes them to make the analysis more coherent. It also does not directly eliminate redundant data or reduce dimensionality; those tasks are more aligned with data cleaning and dimensionality reduction techniques, respectively. Thus, scaling the data appropriately through normalization is essential for effective data analysis.