Description: Data value normalization is a fundamental process in data preprocessing that involves scaling the values of a dataset to ensure comparability across different datasets. This process is crucial in data analysis and machine learning model building, as algorithms are often sensitive to the scale of the data. Normalization ensures that each feature contributes equally to the analysis, preventing variables with wider ranges from dominating the process. There are various normalization techniques, such as Min-Max normalization, which adjusts values to a specific range, and Z-score normalization, which transforms data to have a mean of zero and a standard deviation of one. Normalization not only improves model accuracy but also facilitates data visualization, allowing for better interpretation of results. In summary, data value normalization is an essential step in preprocessing that ensures comparability and effectiveness in data analysis.
