Email Support Ticket Categorizer
NLP-based support ticket categorization using TF-IDF, Linear SVM, confidence scoring, and priority tagging.
A lightweight NLP-based support ticket categorization system using TF-IDF and Linear SVM with confidence scoring, priority tagging, and a Streamlit demo. Designed for fast, reliable ticket routing without large model overhead.
Problem Statement
Support teams spend significant time manually triaging and routing incoming tickets, especially under high volume.
System Solution
A trained TF-IDF + Linear SVM pipeline that categorizes incoming tickets, assigns confidence scores, tags priority, and routes automatically — fast enough to run at inbox-level throughput.
Architecture & Data Flow
TF-IDF vectorization of email/ticket text for feature extraction.
Linear SVM classifier trained on labeled support ticket datasets.
Confidence scoring with fallback routing for low-confidence predictions.
Priority tagging based on category and keyword heuristics.
Streamlit demo for live categorization testing.
Key Technical Features
- 01.Multi-class ticket categorization with confidence thresholds.
- 02.Priority tagging (low/medium/high) based on predicted category.
- 03.Streamlit interface for interactive demo and testing.
- 04.Lightweight pipeline — no GPU or large model required.
Engineering Challenges
- 01.Handling imbalanced ticket categories in training data.
- 02.Maintaining accuracy at category boundaries where tickets overlap multiple domains.
- 03.Designing a useful confidence fallback for ambiguous inputs.