minimum for each feature maximum for each feature in[ ]use the same transformation on the test setusing min and range of the training set (see for detailsx_test_scaled (x_test min_on_trainingrange_on_training in[ ]svc svc(svc fit(x_train_scaledy_trainprint("accuracy on training set{ }formatsvc score(x_train_scaledy_train))print("accuracy on test set{ }format(svc score(x_test_scaledy_test))out[ ]accuracy on training set accuracy on test set scaling the data made huge differencenow we are actually in an underfitting regimewhere training and test set performance are quite similar but less close to accuracy from herewe can try increasing either or gamma to fit more complex model for examplein[ ]svc svc( = svc fit(x_train_scaledy_trainprint("accuracy on training set{ }formatsvc score(x_train_scaledy_train))print("accuracy on test set{ }format(svc score(x_test_scaledy_test))out[ ]accuracy on training set accuracy on test set hereincreasing allows us to improve the model significantlyresulting in accuracy supervised machine learning algorithms