now that we have successfully scaled our numeric features (see figure - )let' handle our categorical features and carry out the necessary feature engineering needed based on the following code in [ ]training_features pd get_dummies(training_featurescolumns=categoricial_feature_namesview newly engineering features training_features figure - feature set with engineered categorical variables in [ ]get list of new categorical features categorical_engineered_features list(set(training_features columnsset(numeric_feature_names)figure - shows us the updated feature set with the newly engineered categorical variables this process is also known as one hot encoding modeling we will now build simple classification (supervisedmodel based on our feature set by using the logistic regression algorithm the following code depicts how to build the supervised model in [ ]from sklearn linear_model import logisticregression import numpy as np fit the model lr logisticregression(model lr fit(training_featuresnp array(outcome_labels['recommend'])view model parameters model out[ ]logisticregression( = class_weight=nonedual=falsefit_intercept=trueintercept_scaling= max_iter= multi_class='ovr'n_jobs= penalty=' 'random_state=nonesolver='liblinear'tol= verbose= warm_start=falsethuswe now have our supervised learning model based on the logistic regression model with regularizationas you can see from the parameters in the previous output