What would you like to understand?

Min-Max normalization is a method that linearly transforms raw data into the interval [0, 1]. The formula is $X' = \frac{X - \min}{\max - \min}$, where $X$ is the original value, and $\min$ and $\max$ are the minimum and maximum values of that feature column, respectively.

Conditions: The dataset has clear min and max values.; max is not equal to min

Min-Max normalization is a method that linearly transforms raw data into the interval [0, 1]. The formula is $X' = \frac{X - \min}{\max - \min}$, where $X$ is the original value, and $\min$ and $\max$ are the minimum and maximum values of that feature column, respectively.

Conditions: The dataset has clear min and max values.; max is not equal to min