code: public void _predict(Dataframe newData) {
          //load all trainables on the bundles
          initBundle();
  
          //set the parallized flag to all algorithms
          bundle.setParallelized(isParallelized());
  
          //run the pipeline
          for(String step : pipeline) {
              switch (step) {
                  case NS_KEY:
                      AbstractScaler numericalScaler = (AbstractScaler) bundle.get(NS_KEY);
                      if(numericalScaler != null) {
                          numericalScaler.transform(newData);
                      }
                      break;
                  case CE_KEY:
                      AbstractEncoder categoricalEncoder = (AbstractEncoder) bundle.get(CE_KEY);
                      if(categoricalEncoder != null) {
                          categoricalEncoder.transform(newData);
                      }
                      break;
                  case FS_KEY:
                      int numOfFS = getTrainingParameters().getFeatureSelectorTrainingParametersList().size();
                      for(int i=0;i<numOfFS;i++) {
                          AbstractFeatureSelector featureSelector = (AbstractFeatureSelector) bundle.get(FS_KEY+i);
                          featureSelector.transform(newData);
                      }
                      break;
                  case ML_KEY:
                      AbstractModeler modeler = (AbstractModeler) bundle.get(ML_KEY);
                      modeler.predict(newData);
                      break;
                  default:
                      throw new RuntimeException("Invalid Pipeline Step");
              }
          }
      }
docs: {@inheritDoc}