between and finallythe normalizer does very different kind of rescaling it scales each data point such that the feature vector has euclidean length of in other wordsit projects data point on the circle (or spherein the case of higher dimensionswith radius of this means every data point is scaled by different number (by the inverse of its lengththis normalization is often used when only the direction (or angleof the data mattersnot the length of the feature vector applying data transformations now that we've seen what the different kinds of transformations dolet' apply them using scikit-learn we will use the cancer dataset that we saw in preprocessing methods like the scalers are usually applied before applying supervised machine learning algorithm as an examplesay we want to apply the kernel svm (svcto the cancer datasetand use minmaxscaler for preprocessing the data we start by loading our dataset and splitting it into training set and test set (we need separate training and test sets to evaluate the supervised model we will build after the preprocessing)in[ ]from sklearn datasets import load_breast_cancer from sklearn model_selection import train_test_split cancer load_breast_cancer(x_trainx_testy_trainy_test train_test_split(cancer datacancer targetrandom_state= print(x_train shapeprint(x_test shapeout[ ]( ( as reminderthe dataset contains data pointseach represented by measurements we split the dataset into samples for the training set and samples for the test set as with the supervised models we built earlierwe first import the class that implements the preprocessingand then instantiate itin[ ]from sklearn preprocessing import minmaxscaler scaler minmaxscaler( unsupervised learning and preprocessing