arithmic scalepreprocessing data for svms one way to resolve this problem is by rescaling each feature so that they are all approximately on the same scale common rescaling method for kernel svms is to scale the data such that all features are between and we will see how to do this using the minmaxscaler preprocessing method in where we'll give more details for nowlet' do this "by hand"in[ ]compute the minimum value per feature on the training set min_on_training x_train min(axis= compute the range of each feature (max minon the training set range_on_training (x_train min_on_trainingmax(axis= subtract the minand divide by range afterwardmin= and max= for each feature x_train_scaled (x_train min_on_trainingrange_on_training print("minimum for each feature\ {}format(x_train_scaled min(axis= ))print("maximum for each feature\ {}format(x_train_scaled max(axis= )) supervised learning