Normalizing data

Normalizing data is a data preprocessing technique that involves scaling the values of a variable to a standard range. This is done to eliminate the effects of different scales of the variables and to make the data more consistent for analysis. There are different methods for normalizing data, including Min-Max normalization and Z-score normalization.

In Min-Max normalization, the values of a variable are rescaled to a range between 0 and 1. This is done by subtracting the minimum value of the variable from each value and then dividing by the range (the difference between the maximum and minimum values).

In Z-score normalization, the values of a variable are rescaled to a standard normal distribution with a mean of 0 and a standard deviation of 1. This is done by subtracting the mean of the variable from each value and then dividing by the standard deviation.

Normalizing data helps in improving the performance of machine learning models, especially those that are sensitive to the scale of the variables, such as K-nearest neighbors and support vector machines.