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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 linear transformation that scales the raw feature values to a fixed range, typically [0, 1]. It subtracts the minimum value of the feature from each data point and then divides by the range (maximum minus minimum).

Conditions: Applied to a continuous numerical feature.; Requires knowing the minimum and maximum values of the feature.; The maximum and minimum values must not be equal (to avoid division by zero).

Min-max normalization is a linear transformation that scales the raw feature values to a fixed range, typically [0, 1]. It subtracts the minimum value of the feature from each data point and then divides by the range (maximum minus minimum).

Conditions: Applied to a continuous numerical feature.; Requires knowing the minimum and maximum values of the feature.; The maximum and minimum values must not be equal (to avoid division by zero).