types of normalization in machine learningthere are two types of normalization preprocessing techniques as followsl normalization it may be defined as the normalization technique that modifies the dataset values in way that in each row the sum of the absolute values will always be up to it is also called least absolute deviations example in this examplewe use normalize technique to normalize the data of pima indians diabetes dataset which we used earlier firstthe csv data will be loaded and then with the help of normalizer class it will be normalized the first few lines of following script are same as we have written in previous while loading csv data from pandas import read_csv from numpy import set_printoptions from sklearn preprocessing import normalizer path ' :\pima-indians-diabetes csvnames ['preg''plas''pres''skin''test''mass''pedi''age''class'dataframe read_csv (pathnames=namesarray dataframe values nowwe can use normalizer class with to normalize the data data_normalizer normalizer(norm=' 'fit(arraydata_normalized data_normalizer 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 ("\nnormalized data:\ "data_normalized [ : ]output normalized data[[ [ [ ]