Description: Mean normalization is a data preprocessing technique used to center data around its mean. This process involves subtracting the mean from each value in a dataset, resulting in a new dataset where the mean is equal to zero. This technique is particularly useful in the context of data analysis and machine learning, as it helps eliminate biases in the data and improves the convergence of optimization algorithms. By centering the data, it facilitates comparison between different features and reduces the influence of variable magnitudes, which can be crucial in models sensitive to data scaling. Mean normalization is often a preliminary step to other normalization techniques, such as standard deviation normalization, and is commonly used in conjunction with other data transformations to prepare datasets for analysis. In summary, mean normalization is a fundamental tool in data preprocessing that allows for better interpretation and analysis of data.
