Description: Volume normalization is the process of adjusting data volumes to a standard scale, allowing different data sets to be comparable and effectively integrated into subsequent analyses. This process is fundamental in data preprocessing, as it helps eliminate biases that may arise from variations in data magnitudes. Normalization is carried out using various techniques, such as min-max normalization, which scales data to a specific range, or Z-score normalization, which adjusts data based on its mean and standard deviation. By applying these techniques, the goal is to achieve a more uniform distribution of data, facilitating interpretation and analysis. Volume normalization is especially relevant in contexts where multiple variables may have different units of measurement or scales, such as in financial, scientific, or machine learning data analysis. By standardizing volumes, the accuracy of predictive models is improved, and the performance of machine learning algorithms is optimized, allowing them to learn more relevant patterns without being influenced by the magnitude of the original data.
