figure - updated feature set for new students we now have the relevant features for the new studentshowever you can see that some of the categorical features are missing based on some grades like bcand this is because none of these students obtained those grades but we still need those attributes because the model was trained on all attributes including these the following snippet helps us identify and add the missing categorical features we add the value for each of those features as for each student since they did not obtain those grades in [ ]add missing categorical feature columns current_categorical_engineered_features set(prediction_features columnsset(numeric_feature_namesmissing_features set(categorical_engineered_featurescurrent_categorical_engineered_features for feature in missing_featuresadd zeros since feature is absent in these data samples prediction_features[feature[ len(prediction_featuresview final feature set prediction_features figure - final feature set for new students we have our complete feature set ready for both the new students let' put our model to the test and get the predictions with regard to grant recommendationsin [ ]#predict using model predictions model predict(prediction_features#display results new_data['recommend'predictions new_data