Neural-Network-Ticketing-Routing-Agent

Neural-Network-Ticketing-Routing-Agent is a neural-network-based ticketing routing agent. The agent is trained and tested with a multilayer feedforward neural network, and interacts with a user through a command-line interface, allowing the agent to ask the user questions to create a new ticket, with the capacity to make early predictions and retrain if the user comes up with a new combination of answers for a ticket. The optimal parameters are found with a grid search algorithm that tests 12,600 unique combinations of parameters (over 5 runs for even 80%/20% data splits), narrowing the neural network down to 14 optimal combinations. The agent is developed in Python 3.7 using the Scikit-Learn, NumPy, Pandas and Matplotlib libraries.

The report, which includes a summary of features implemented, design & implementation decisions (data encoding and training/testing split), evaluation (training/testing result visualisation in plots and heatmaps, grid search algorithm for determining optimal hyperparameters) and testing sections, can be read here.

Usage

Clone the repository (or download the zipped project): $ git clone https://github.com/Adamouization/Neural-Network-Ticketing-Routing-Agent

Create a virtual environment for the project and activate it:

virtualenv ~/Environments/Neural-Network-Ticketing-Routing-Agent
source Neural-Network-Ticketing-Routing-Agent/bin/activate

Once you have the virtualenv activated and set up, cd into the project directory and install the requirements needed to run the app:

pip install -r requirements.txt

You can now run the app:

python A4Main.py [-h] -a AGENT -c CSV [-g] [-d]

where:

• AGENT is the type of agent to run: [Bas, Int, Adv]:
  • Bas: Train and test the neural network with the optimal parameters, or run the Grid Search algorithm to determine the optimal parameters.
  • Int: CLI text-based application to submit a new ticket and predict to which response team it should go.
  • Adv: Train and test a decision tree classifier.
• CSV is the CSV file containing the data used to train/test the data.
• -g: flag set to run the grid search algorithm.
• -d: flag set to enter debug mode, printing more statements to the command line.
• -h: flag for help on how to use the agent (prints instructions on the command line).

Examples:

• python A4Main.py -a Bas -c tickets -d to train/test the neural network.
• python A4Main.py -a Bas -c tickets -g to run the grid search algorithm.
• python A4Main.py -a Int -c tickets to submit a new ticket through the CLI text-based interface.
• python A4Main.py -a Adv -c tickets to train/test the decision tree.
• python A4Main.py -h for help on how to run the agent.

Contact

• Email: adam@jaamour.com
• Website: www.adam.jaamour.com
• LinkedIn: @adamjaamour
• Twitter: @Adamouization