-* If RF model is not present in the path, run train() to train the model for the prediction.
- Call predict function for every 1 second(for now as we are using simulated data).
-* Read the input csv file that has both normal and anomalous data.
-* Simulate diff UEIDs that participate in the anomaly by randomly selecting records from this scoring data set
-* Send the UEID and timestamp for the anomalous entries to the Traffic Steering (rmr with the message type as 30003)
-* Get the acknowledgement message from the traffic steering.
+* If hdbscan is not present in the current path, run train() to train the model for the prediction.
+* Call predict function to perform the following activities for every 1 second.
+ a) Read the input csv file( 1000 UEID samples)
+ b) Predict the anomaly records for the randomly selected UEID
+ c) send the UEID and timestamp for the anomalous entries to the Traffic Steering (rmr with the message type as 30003)
+ d) Get the acknowledgement message from the traffic steering.