names ['preg''plas''pres''skin''test''mass''pedi''age''class'dataframe read_csv(pathnames=namesarray dataframe values nowwe can use minmaxscaler class to rescale the data in the range of and data_scaler preprocessing minmaxscaler(feature_range=( , )data_rescaled data_scaler fit_transform(arraywe can also summarize the data for output as per our choice herewe are setting the precision to and showing the first rows in the output set_printoptions(precision= print ("\nscaled data:\ "data_rescaled[ : ]output scaled data[[ [ [ [ [ [ [ [ [ [ ]from the above outputall the data got rescaled into the range of and normalization another useful data preprocessing technique is normalization this is used to rescale each row of data to have length of it is mainly useful in sparse dataset where we have lots of zeros we can rescale the data with the help of normalizer class of scikit-learn python library