Hello!
I believe I solved my problem. . . . I found another data set consisting of 4,200 instances which are well balanced between the two classes and configured my workflow in such a way that normalization happens after the partition and also configured the flow variables corresponding to the best parameters as determined by parametric tuning in the training/validation set to the testing set. The end result is that the accuracy of the training/validation set hovered between 92 and 93% with the test accuracy consistently about 2 points lower along with precision, recall and ROC(AUC) being greater than 0.9.
Thank you again for your assistance and patience during this time period. Much appreciated!
~Cole K.