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To standardize a dataset using the Z-score formula, first compute the mean ($\mu$) and standard deviation ($\sigma$) of the dataset. Then, for each original data value $X$, apply the formula $X' = \frac{X - \mu}{\sigma}$ to convert it into a standardized score.

Conditions: Requires knowing the mean and standard deviation of the dataset

To standardize a dataset using the Z-score formula, first compute the mean ($\mu$) and standard deviation ($\sigma$) of the dataset. Then, for each original data value $X$, apply the formula $X' = \frac{X - \mu}{\sigma}$ to convert it into a standardized score.

Conditions: Requires knowing the mean and standard deviation of the dataset

Import `StandardScaler` from `sklearn.preprocessing`, instantiate it, and call `fit_transform` on the selected DataFrame columns. This transforms the data to have mean 0 and standard deviation 1.

Conditions: Input data is numerical.; Scikit-learn library is installed.

Import `StandardScaler` from `sklearn.preprocessing`, instantiate it, and call `fit_transform` on the selected DataFrame columns. This transforms the data to have mean 0 and standard deviation 1.

Conditions: Input data is numerical.; Scikit-learn library is installed.

Standard deviation is calculated by finding the squared differences from the mean, averaging those squared differences to get the variance, and then taking the square root of the variance. The formula is $\sigma = \sqrt{\frac{\sum (X - \mu)^2}{N}}$.

Conditions: The mean $\mu$ of the dataset has already been calculated.

Standard deviation is calculated by finding the squared differences from the mean, averaging those squared differences to get the variance, and then taking the square root of the variance. The formula is $\sigma = \sqrt{\frac{\sum (X - \mu)^2}{N}}$.

Conditions: The mean $\mu$ of the dataset has already been calculated.

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