min-max scalar the min-max scalar form of normalization uses the mean and standard deviation to box all the data into range lying between certain min and max value for most purposesthe range is set between and at other timesother ranges may be applied but the to range remains the defaultscaled_values preprocessing minmaxscaler(feature_range=( , )results scaled_values fit(datatransform(dataprint(resultsan instance of the minmaxscaler class is created with the range ( , and passed to the variable scaled_values the fit function is called to make the necessary calculations that will be used internally to change the dataset the transform function effects the actual operation on the datasetreturning the value to results[ ]we can see from the preceding output that all the data is normalized and lies between and this kind of output can now be supplied to machine learning algorithm standard scalar the mean values for the respective features in our initial dataset or table are and to make all the data have similar meanthat isa zero mean and unit variance across the datawe shall apply the standard scalar algorithmstand_scalar preprocessing standardscaler(fit(dataresults stand_scalar transform(dataprint(resultsdata is passed to the fit method of the object returned from instantiating the standardscaler class the transform method acts on the data elements in the data and returns the output to the results[ - [- [- - - ]examining the resultswe observe that all our features are now evenly distributed