figure - raw data depicting student records and their recommendations now that we can see data samples showing records for each student and their corresponding recommendation outcomes in figure - we will perform necessary tasks relevant to data preparation data preparation based on the dataset we saw earlierwe do not have any data errors or missing valueshence we will mainly focus on feature engineering and scaling in this section feature extraction and engineering let' start by extracting the existing features from the dataset and the outcomes in separate variables the following snippet shows this process see figures - and - in [ ]get features and corresponding outcomes feature_names ['overallgrade''obedient''researchscore''projectscore'training_features df[feature_namesoutcome_name ['recommend'outcome_labels df[outcome_namein [ ]view features training_features