The AI-Powered Support Ticket Classification System is a machine learning application designed to automatically analyze, categorize, and prioritize customer support tickets.
Modern businesses receive thousands of customer support requests daily through email, chat systems, and helpdesk platforms. Manually reviewing and categorizing these tickets can be time-consuming, inefficient, and prone to errors.
This project leverages Natural Language Processing (NLP) and Machine Learning to build an intelligent system that can:
- Automatically classify support tickets into categories
- Estimate the priority level of each ticket
- Provide an interactive analytics dashboard
- Train a machine learning model for improved accuracy
- Suggest responses for faster customer service
The application is built using Python, Streamlit, Scikit-Learn, Pandas, and Plotly, making it a powerful and easy-to-use AI-powered helpdesk analytics tool.
The application includes a real-time analytics dashboard that visualizes support ticket data and insights.
Dashboard metrics include:
- Total number of tickets
- Number of ticket categories
- High-priority ticket count
- Average ticket text length
Visualizations include:
- Category distribution chart
- Priority breakdown chart
- Category vs Priority heatmap
- Raw dataset preview
These insights help support teams identify patterns and manage workloads efficiently.
Users can upload their own CSV dataset of support tickets.
The system automatically:
- Detects text columns containing ticket descriptions
- Allows users to select the appropriate column
- Classifies all tickets automatically
- Displays classification results in real-time
This enables quick processing of large-scale customer support datasets.
The project supports two classification approaches.
When no machine learning model is trained, the system uses a rule-based keyword classifier.
It detects important keywords in the ticket text and assigns categories accordingly.
Example keyword mappings:
| Category | Example Keywords |
|---|---|
| Billing | bill, payment, invoice, refund, charge |
| Technical | error, bug, crash, not working |
| Account | login, password, account access |
| Shipping | delivery, package, tracking |
| Returns | refund, exchange, damaged item |
| General Inquiry | question, help, information |
This ensures the system works immediately even without training a model.
For higher accuracy, the system allows training a machine learning classifier.
The model pipeline includes:
Ticket Text
↓
TF-IDF Vectorization
↓
Logistic Regression Classifier
↓
Predicted Ticket Category
This approach allows the model to learn patterns from historical ticket data and improve prediction accuracy.
The machine learning pipeline includes several important steps.
The ticket text is cleaned and prepared for machine learning using:
- Lowercasing
- Tokenization
- Stop-word removal
- Text vectorization
Text features are extracted using TF-IDF (Term Frequency – Inverse Document Frequency).
TF-IDF helps identify important words in ticket descriptions.
Configuration includes:
- Maximum feature size
- N-gram range (1,2)
- Sublinear term frequency scaling
The project uses Logistic Regression as the classifier.
Advantages of Logistic Regression for text classification:
- Fast training
- Good accuracy
- Interpretable model coefficients
- Works well with TF-IDF features
The classifier predicts the category of each support ticket.
The system supports the following categories:
- Billing Issues
- Technical Problems
- Account Management
- Shipping & Delivery
- Returns and Refunds
- General Inquiry
- Other Issues
These categories help organize support requests and route them to the appropriate teams.
The system also estimates the priority level of each support ticket.
Priority levels include:
Detected using keywords such as:
- urgent
- critical
- immediately
- emergency
- cannot access
- fraud
These tickets require immediate attention.
Default level for standard support requests.
Detected using phrases such as:
- just asking
- no rush
- whenever possible
- curious about
These tickets can be handled after urgent requests.
After training the machine learning model, the system displays detailed evaluation metrics.
Evaluation includes:
- Accuracy
- Precision
- Recall
- F1 Score
These metrics measure how well the model predicts ticket categories.
A confusion matrix visualizes prediction performance across categories, showing where the model performs well or makes mistakes.
The system identifies the most important words influencing predictions.
Example:
| Category | Important Terms |
|---|---|
| Billing | payment, invoice, charge |
| Technical | error, bug, crash |
| Shipping | delivery, tracking |
| Account | login, password |
This helps interpret how the machine learning model works.
The system also provides automated response suggestions for different ticket categories.
Example:
Billing Issue Response
Hi! I can see you have a billing concern.
I'll review your account charges and follow up within 24 hours.
Technical Issue Response
Thanks for reaching out!
Our technical team has been notified. Could you provide screenshots or error messages?
These responses help reduce support response time.
Users can also paste individual support messages into the system.
Example input:
I was charged twice for my subscription and need a refund immediately.
Example output:
Predicted Category: Billing
Estimated Priority: High
The system also displays confidence scores for predictions.
The Streamlit application contains four main sections.
Displays analytics and visualizations for support tickets.
Allows users to upload CSV datasets and automatically classify tickets.
Users can train a machine learning model using labelled data.
The training interface allows configuration of:
- Test split percentage
- TF-IDF feature size
- Training dataset columns
Users can manually enter ticket text to get instant predictions.
FUTURE_ML_02
│
├── Support Ticket Classification.ipynb
├── Ticket_app.py
├── customer_support_tickets.csv
├── README.md
├── LICENSE
File descriptions:
Support Ticket Classification.ipynb
Notebook used for experimentation and model development.
Ticket_app.py
Main Streamlit application.
customer_support_tickets.csv
Sample dataset used for training and testing.
README.md
Project documentation.
LICENSE
MIT License for open-source usage.
git clone https://github.com/HarshaVardhanM08/FUTURE_ML_02.git
cd FUTURE_ML_02
pip install streamlit pandas scikit-learn plotly
streamlit run Ticket_app.py
The application will open in your browser at:
http://localhost:8501
| Technology | Purpose |
|---|---|
| Python | Core programming language |
| Streamlit | Web application framework |
| Pandas | Data manipulation |
| Scikit-Learn | Machine learning algorithms |
| Plotly | Interactive data visualization |
| TF-IDF | Feature extraction |
| Logistic Regression | Text classification |
This system can be used in:
- Customer Support Platforms
- Helpdesk Automation Systems
- IT Service Management
- SaaS Customer Support
- AI Chatbots
- CRM systems
It can significantly improve:
- Ticket routing
- Response time
- Customer satisfaction
- Support team efficiency
Possible improvements include:
- Deep Learning models (BERT, Transformers)
- Multi-language ticket support
- Integration with CRM systems
- Real-time email ticket processing
- AI-generated automated replies
- Cloud deployment for production use
This project is licensed under the MIT License.
Harsha Vardhan Maradana
Python Full Stack Developer (In Progress) | Game Development Enthusiast | Focused on Creating Immersive & Innovative Digital Experiences
GitHub
https://github.com/HarshaVardhanM08
If you found this project helpful:
⭐ Star the repository
🍴 Fork the project
📢 Share it with others