step applying preprocessing techniquein this stepwe need to apply any of the preprocessing techniques the following section describes the data preprocessing techniques techniques for data preprocessing the techniques for data preprocessing are described belowbinarization this is the preprocessing technique which is used when we need to convert our numerical values into boolean values we can use an inbuilt method to binarize the input data say by using as the threshold value in the following waydata_binarized preprocessing binarizer(threshold= transform(input_data print("\nbinarized data:\ "data_binarizednowafter running the above code we will get the following outputall the values above (threshold valuewould be converted to and all the values below would be converted to binarized data [ ]mean removal it is another very common preprocessing technique that is used in machine learning basically it is used to eliminate the mean from feature vector so that every feature is centered on zero we can also remove the bias from the features in the feature vector for applying mean removal preprocessing technique on the sample datawe can write the python code shown below the code will display the mean and standard deviation of the input dataprint("mean ="input_data mean(axis= )print("std deviation "input_data std(axis= )we will get the following output after running the above lines of codemean std deviation -