maxscalerthe fit method computes the minimum and maximum value of each feature on the training set in contrast to the classifiers and regressors of the scaler is only provided with the data (x_trainwhen fit is calledand y_train is not usedin[ ]scaler fit(x_trainout[ ]minmaxscaler(copy=truefeature_range=( )to apply the transformation that we just learned--that isto actually scale the training data--we use the transform method of the scaler the transform method is used in scikit-learn whenever model returns new representation of the datain[ ]transform data x_train_scaled scaler transform(x_trainprint dataset properties before and after scaling print("transformed shape{}format(x_train_scaled shape)print("per-feature minimum before scaling:\ {}format(x_train min(axis= ))print("per-feature maximum before scaling:\ {}format(x_train max(axis= ))print("per-feature minimum after scaling:\ {}formatx_train_scaled min(axis= ))print("per-feature maximum after scaling:\ {}formatx_train_scaled max(axis= ))out[ ]transformed shape( per-feature minimum before scaling per-feature maximum before scaling per-feature minimum after scaling per-feature maximum after scaling preprocessing and scaling