Description: Feature normalization is a fundamental process in data preprocessing that involves scaling the features of a dataset so that they contribute equally to distance calculations in machine learning algorithms. This process is crucial, especially in algorithms that rely on distance, such as K-nearest neighbors (KNN) and support vector machines (SVM). Normalization ensures that features with different scales do not dominate the analysis, which could lead to biased results. There are several normalization methods, with the most common being min-max normalization, which scales 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 normalization method depends on the type of data and the algorithm that will be used subsequently. In summary, feature normalization is an essential step to improve the accuracy and effectiveness of machine learning models, ensuring that each feature has a proportional impact on the final outcome.
