nowthe code below will remove the mean and standard deviation of the input datadata_scaled preprocessing scale(input_dataprint("mean ="data_scaled mean(axis= )print("std deviation ="data_scaled std(axis= )we will get the following output after running the above lines of codemean - std deviation + + scaling it is another data preprocessing technique that is used to scale the feature vectors scaling of feature vectors is needed because the values of every feature can vary between many random values in other words we can say that scaling is important because we do not want any feature to be synthetically large or small with the help of the following python codewe can do the scaling of our input datai feature vectormin max scaling data_scaler_minmax preprocessing minmaxscaler(feature_range=( , )data_scaled_minmax data_scaler_minmax fit_transform(input_dataprint ("\nmin max scaled data:\ "data_scaled_minmaxwe will get the following output after running the above lines of codemin max scaled data [ ]normalization it is another data preprocessing technique that is used to modify the feature vectors such kind of modification is necessary to measure the feature vectors on common scale followings are two types of normalization which can be used in machine learningl normalization it is also referred to as least absolute deviations this kind of normalization modifies the values so that the sum of the absolute values is always up to in each row it can be implemented on the input data with the help of the following python codenormalize data data_normalized_l preprocessing normalize(input_datanorm=' '