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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](https://scikit-learn.org), NumPy, Pandas and Matplotlib libraries.\n\nThe report, which includes a summary of features implemented, design \u0026 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](https://github.com/Adamouization/Neural-Network-Ticketing-Routing-Agent/blob/master/report/report.pdf).\n\n## Usage\n\nClone the repository (or download the zipped project):\n`$ git clone https://github.com/Adamouization/Neural-Network-Ticketing-Routing-Agent`\n\nCreate a virtual environment for the project and activate it:\n\n```\nvirtualenv ~/Environments/Neural-Network-Ticketing-Routing-Agent\nsource Neural-Network-Ticketing-Routing-Agent/bin/activate\n```\n\nOnce you have the virtualenv activated and set up, `cd` into the project directory and install the requirements needed to run the app:\n\n```\npip install -r requirements.txt\n```\n\nYou can now run the app:\n```\npython A4Main.py [-h] -a AGENT -c CSV [-g] [-d]\n```\n\nwhere:\n\n* `AGENT` is the type of agent to run: `[Bas, Int, Adv]`:\n    * `Bas`: Train and test the neural network with the optimal parameters, or run the Grid Search algorithm to determine the optimal parameters.\n    * `Int`: CLI text-based application to submit a new ticket and predict to which response team it should go.\n    * `Adv`: Train and test a decision tree classifier.\n* `CSV` is the CSV file containing the data used to train/test the data.\n* `-g`: flag set to run the grid search algorithm.\n* `-d`: flag set to enter debug mode, printing more statements to the command line.\n* `-h`: flag for help on how to use the agent (prints instructions on the command line).\n\nExamples:\n* `python A4Main.py -a Bas -c tickets -d` to train/test the neural network.\n* `python A4Main.py -a Bas -c tickets -g` to run the grid search algorithm.\n* `python A4Main.py -a Int -c tickets` to submit a new ticket through the CLI text-based interface.\n* `python A4Main.py -a Adv -c tickets` to train/test the decision tree.\n* `python A4Main.py -h` for help on how to run the agent.\n\n## Contact\n* Email: adam@jaamour.com\n* Website: www.adam.jaamour.com\n* LinkedIn: [@adamjaamour](https://www.linkedin.com/in/adamjaamour/)\n* Twitter: [@Adamouization](https://twitter.com/Adamouization)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadamouization%2Fneural-network-ticketing-routing-agent","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fadamouization%2Fneural-network-ticketing-routing-agent","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadamouization%2Fneural-network-ticketing-routing-agent/lists"}