Description: Data point normalization is the process of adjusting individual data points to a common scale to facilitate more effective and accurate analysis. This procedure is fundamental in data preprocessing, as it allows different variables, which may have disparate ranges and units of measurement, to be comparable to each other. Normalization helps mitigate the impact of differences in data scale, which can distort the results of statistical analyses or machine learning algorithms. There are various normalization techniques, such as Min-Max normalization, which adjusts data to a specific range, and Z-score normalization, which transforms data to have a mean of zero and a standard deviation of one. The choice of the appropriate technique depends on the context and the nature of the data. In summary, data point normalization is a crucial stage in data analysis, as it improves the quality of results and allows for clearer interpretation of information.
