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Service Desk Ticket Classifier is an intelligent system that uses a Convolutional Neural Network (CNN) to classify service desk tickets into predefined categories and automatically route them to the appropriate departments via email. This streamlines customer service processes, improves response time, and enhances operational efficiency.

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KunalParkhade/service-desk-ticket-classification

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🛠️Service Desk Ticket Classifier

The Service Desk Ticket Classifier is an intelligent system that uses a Convolutional Neural Network (CNN) to classify service desk tickets into predefined categories and automatically route them to the appropriate departments via email. This streamlines customer service processes, improves response time, and enhances operational efficiency.

Features 🌟

  • Ticket Classification: 🏷️Categorizes service desk tickets into predefined categories such as Technical Issues, Account Issues, Billing & Payments, General Inquiries, and Service Requests.
  • Automatic Email Routing: 📧Routes tickets to the concerned department emails automatically.
  • Interactive User Interface: 👩‍💻Provides a user-friendly interface built with Streamlit, featuring multiple tabs for seamless navigation.
  • Customizable Categories: 🔧Easily modify categories to suit your organization’s needs.

Project Structure 📂

service-desk-ticket-classification/
├── app.py                # Main application file (Streamlit UI)
├── model.py              # CNN model definition
├── data/
│   ├── words.json        # Word-to-index mapping
│   ├── text.json
│   ├── labels.npy        # Predefined ticket labels
│   ├── testing.jpg       # Banner image for UI
├── saved_models/
│   └── model.pth         # Trained model weights
├── requirements.txt      # Python dependencies
├── train.py
├── .gitignore
├── README.md             # Project documentation

Installation 🚀

  1. Clone the repository:

    git clone https://github.com/KunalParkhade/service-desk-ticket-classification.git
    cd service-desk-ticket-classifier
  2. Install the required dependencies:

    pip install -r requirements.txt
  3. Download the pretrained model weights and place them in the saved_models/ directory.

  4. Run the application:

    streamlit run app2.py

How to Use 📝

1. Ticket Classification 🎟️

  • Navigate to the Home tab.
  • Enter the ticket description in the text area.
  • Click on the "Classify Ticket" button to get the predicted category.

2. Email Routing 📤

  • Ensure email configurations (SMTP server, credentials, etc.).
  • On ticket classification, the system automatically sends the ticket to the relevant department email.

3. Explore Tabs 🌍

  • Home: 🏠Homepage
  • Ticket Classification: 📝Classify tickets and view results.
  • About the Model: 🔍Model details
  • Contact Us: 📞Contact details of the company given

Categories 📑

The predefined ticket categories are:

  1. Technical Issues🖥️: Issues related to hardware, software, or network.
  2. Account Issues🔑: Queries or requests related to accounts of users..
  3. Billing & Payment💳: Issues regarding payments.
  4. General Queries❓: Any other inquiries or tickets.
  5. Service Requests🛠️: Any service requests.

Screenshots 📸

Home Banner

Future Enhancements 🔮

  • Support for multilingual ticket classification.
  • Integration with ticket management platforms like Jira or ServiceNow. 🔗
  • Advanced analytics for ticket trends and response metrics.📊

Contributing 🤝

We welcome contributions! To contribute:

  1. Fork the repository.
  2. Create a new branch for your feature 🌱.
  3. Submit a pull request with a clear description of your changes.⬇️

License 📜

This project is licensed under the MIT License.

Contact 📧

For any queries or suggestions, please contact:


Streamline your service desk operations with the power of AI! 🤖

About

Service Desk Ticket Classifier is an intelligent system that uses a Convolutional Neural Network (CNN) to classify service desk tickets into predefined categories and automatically route them to the appropriate departments via email. This streamlines customer service processes, improves response time, and enhances operational efficiency.

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