Description: Attribute normalization is the process of adjusting values in a dataset to a common scale, which is fundamental in data preprocessing. This procedure aims to eliminate scale differences between variables, allowing each attribute to contribute equally to analysis and predictive model building. Normalization is especially relevant in algorithms that depend on the distance between points, such as k-nearest neighbors (k-NN) or principal component analysis (PCA). By bringing data to a uniform scale, the convergence of machine learning algorithms is improved, and it prevents features with wider ranges from dominating the modeling 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 into a distribution with a mean of zero and a standard deviation of one. The choice of normalization method depends on the type of data and the algorithms being used, making it a crucial step to ensure the effectiveness and accuracy of analytical models.
