standardized scaling the standard scaler tries to standardize each value in feature column by removing the mean and scaling the variance to be from the values this is also known as centering and scaling and can be denoted mathematically as ss mx sx where each value in feature is subtracted by the mean mx and the resultant is divided by the standard deviation sx this is also popularly known as -score scaling you can also divide the resultant by the variance instead of the standard deviation if needed the following snippet helps us achieve this in [ ]ss standardscaler(views['zscore'ss fit_transform(views[['views']]views out[ ]views zscore - - - - - we can see the standardized and scaled values in the zscore column in the preceding dataframe in factyou can manually use the formula we used earlier to compute the same result the following example computes the -score mathematically in [ ]vw np array(views['views'](vw[ np mean(vw)np std(vwout[ ]- min-max scaling with min-max scalingwe can transform and scale our feature values such that each value is within the range of [ however the minmaxscaler class in scikit-learn also allows you to specify your own upper and lower bound in the scaled value range using the feature_range variable mathematically we can represent this scaler as mms min max min where we scale each value in the feature by subtracting it from the minimum value in the feature min (xand dividing the resultant by the difference between the maximum and minimum values in the feature max(xmin (xthe following snippet helps us compute this in [ ]mms minmaxscaler(views['minmax'mms fit_transform(views[['views']]