What are the common techniques used for data standardization?

What are the common techniques used for data standardization?

The common techniques used for data standardization are:

1. Z-score normalization: It transforms the data to have a mean of 0 and a standard deviation of 1 by subtracting the mean and dividing by the standard deviation.

2. Min-max scaling: It scales the data to a specific range, typically between 0 and 1, by subtracting the minimum value and dividing by the range (maximum value minus minimum value).

3. Decimal scaling: It involves dividing the data by a power of 10 to shift the decimal point, making the values fall within a specific range.

4. Log transformation: It applies a logarithmic function to the data, which can help in reducing the skewness and making the distribution more symmetrical.

5. Unit vector scaling: It scales the data to have a unit norm, which means that the length of each data point becomes 1. This technique is commonly used in machine learning algorithms that rely on distance calculations.

These techniques help in standardizing the data, making it easier to compare and analyze across different variables and datasets.