normalization it may be defined as the normalization technique that modifies the dataset values in way that in each row the sum of the squares will always be up to it is also called least squares example in this examplewe use normalization technique to normalize the data of pima indians diabetes dataset which we used earlier firstthe csv data will be loaded (as done in previous 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[[ [ [ ] binarization as the name suggeststhis is the technique with the help of which we can make our data binary we can use binary threshold for making our data binary the values above that threshold value will be converted to and below that threshold will be converted to