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The Z-score technique is best applied when the data is normally distributed, forming a proper bell-shaped curve. It aims to transform data into a distribution with mean 0 and standard deviation 1.

Conditions: The data is normally distributed.

The Z-score technique is best applied when the data is normally distributed, forming a proper bell-shaped curve. It aims to transform data into a distribution with mean 0 and standard deviation 1.

Conditions: The data is normally distributed.

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