now that we have extracted our initial available features from the data and their corresponding outcome labelslet' separate out our available features based on their type (numerical and categoricaltypes of feature variables are covered in more detail in "processingwranglingand visualizing datain [ ]list down features based on type numeric_feature_names ['researchscore''projectscore'categoricial_feature_names ['overallgrade''obedient'we will now use standard scalar from scikit-learn to scale or normalize our two numeric scorebased attributes using the following code in [ ]from sklearn preprocessing import standardscaler ss standardscaler(fit scaler on numeric features ss fit(training_features[numeric_feature_names]scale numeric features now training_features[numeric_feature_namesss transform(training_features[numeric_feature_names]view updated featureset training_features figure - feature set with scaled numeric attributes