columnnormalized
Column normalization, also known as feature scaling, is a technique used in data preprocessing to standardize the range of independent variables or features of data. This process is particularly important in machine learning and data analysis to ensure that each feature contributes equally to the analysis and model training. Column normalization typically involves transforming the data so that each feature has a mean of zero and a standard deviation of one, or scaling the data to a specific range, such as [0, 1].
There are several methods for column normalization, including:
1. Z-score normalization: This method transforms the data to have a mean of zero and a standard
2. Min-max normalization: This method scales the data to a fixed range, usually [0, 1]. It is
3. Decimal scaling: This method moves the decimal point of values of the feature to a suitable
Column normalization is crucial for algorithms that are sensitive to the scale of the data, such as