do remember to have the model_evaluation_utils py module in the same directory where you are running your code since we will be using it for evaluating our predictive models let' briefly look at the workflow we will be following for our predictive systems we will focus on two major phases--model training and model predictions and evaluation figure - workflow blueprint for our wine type and quality classification system from figure - we can see that training data and testing data refer to the wine quality dataset features since we already have the necessary wine attributeswe won' be building additional hand-crafted features labels can be either wine types or quality ratings based on the classification system in the training phasefeature selection will mostly involve selecting all the necessary wine physicochemical attributes and then after necessary scaling we will be training our predictive models for prediction and evaluation in the prediction phase predicting wine types in our wine quality datasetwe have two variants or types of wine--red and white wine the main task of our classification system in this section is to predict the wine type based on other features to start withwe will first select our necessary features and separate out the prediction class labels and prepare train and test datasets we use the prefix wtp_ in our variables to easily identify them as neededwhere wtp depicts wine type prediction in [ ]wtp_features wines iloc[:,:- wtp_feature_names wtp_features columns wtp_class_labels np array(wines['wine_type']