prediction in action we are now ready to start predicting with our newly built and deployed modelto start predictionswe need to load our model and scalar objects into memory the following code helps us do this in [ ]load model and scaler objects model joblib load( 'model/model pickle'scaler joblib load( 'scaler/scaler pickle'we have some sample new student records (for two studentsfor which we want our model to predict if they will get the grant recommendation let' retrieve and view this data using the following code in [ ]#data retrieval new_data pd dataframe([{'name''nathan''overallgrade'' ''obedient'' ''researchscore' 'projectscore' }{'name''thomas''overallgrade'' ''obedient'' ''researchscore' 'projectscore' }]new_data new_data[['name''overallgrade''obedient''researchscore''projectscore']new_data figure - new student records we will now carry out the tasks relevant to data preparation--feature extractionengineeringand scaling--in the following code snippet in [ ]#data preparation prediction_features new_data[feature_namesscaling prediction_features[numeric_feature_namesscaler transform(prediction_features[numeric_feature_names]engineering categorical variables prediction_features pd get_dummies(prediction_featurescolumns=categoricial_feature_namesview feature set prediction_features