Support Ticket Classification & Prioritization
🔍 About the Task
In real companies, customer support teams receive hundreds or thousands of tickets every day — emails, forms, complaints, and issue reports.
The biggest problems are:
- Tickets are not categorized properly
- Urgent issues get delayed
- Support teams waste time sorting instead of solving
Instead of building yet another chatbot, this task focuses on something far more useful and realistic:
👉 Automatically classifying and prioritizing support tickets using Machine Learning.
This is actual ML work used in SaaS companies, service platforms, and internal IT teams.
🎯 Objective
Your goal is to build an ML system that can:
- Read customer support tickets (text)
- Automatically classify them into categories
(e.g., Billing, Technical Issue, Account, General Query) - Assign a priority level
(High / Medium / Low)
This helps businesses respond faster, reduce backlog, and improve customer satisfaction.
✅ What You’ll Do
As part of this task, you will:
- Work with real-world text-based support ticket data
- Clean and preprocess raw text
- Convert text into numerical features
- Train classification models
- Evaluate performance using proper ML metrics
- Explain how this system improves support operations
You are not just training a model —
you are building a decision-support system for businesses.
🛠️ Tools You Can Use (NLP-Focused)
This task intentionally uses different tools than forecasting to expose you to another ML domain.
Core Development Tools
- Python – https://www.python.org
- Jupyter Notebook – https://jupyter.org
- VS Code – https://code.visualstudio.com
NLP & ML Libraries
- NLTK – https://www.nltk.org
- spaCy – https://spacy.io
- Scikit-learn – https://scikit-learn.org
Used for:
- text cleaning
- tokenization
- vectorization (TF-IDF, Bag of Words)
- classification models
📁 Dataset Guidance (Choose Any)
You may use any dataset that represents support tickets, help-desk issues, or customer complaints for text classification and prioritization.
✅ Recommended Datasets
1. Customer Support Ticket Dataset (Kaggle)
Contains structured ticket data including text and categories, perfect for training classification models. Kaggle
🔗 https://www.kaggle.com/datasets/suraj520/customer-support-ticket-dataset
2. IT Service Ticket Classification Dataset (Kaggle)
Large dataset with ticket text and topic labels, ideal for multi-class text classification. Kaggle
🔗 https://www.kaggle.com/datasets/adisongoh/it-service-ticket-classification-dataset
3. Classification of IT Support Tickets (Zenodo)
Open dataset with ~2,229 human-classified support tickets across multiple categories. Zenodo
🔗 https://zenodo.org/records/7648117
⚠️ Extra Option (Customer Complaints, Not Ticket System)
Consumer Complaint Dataset (NLP-ready)
Large dataset of customer complaints that can be used for classification tasks (e.g., product category or issue type). Kaggle
🔗 https://www.kaggle.com/datasets/namigabbasov/consumer-complaint-dataset
✨ Key Features to Implement
Your solution should include:
✔ Text cleaning (lowercasing, stopword removal, punctuation handling)
✔ Feature extraction (TF-IDF / Bag of Words)
✔ Ticket category classification
✔ Priority prediction (High / Medium / Low)
✔ Model evaluation (accuracy, precision, recall)
Optional bonus:
- Confusion matrix
- Class-wise performance analysis
📤 Final Deliverable
You must submit:
- A support ticket classification model
- Clear explanation of:
- how tickets are categorized
- how priority is decided
- Evaluation results and insights
- Clean, well-documented code in a public GitHub repository
Your output should look like something you could confidently explain to:
- a support manager
- a SaaS founder
- a client asking to optimize support operations
🚀 Why This Task Makes You Job-Ready
By completing this task, you will:
- gain hands-on NLP experience
- understand real operational ML use-cases
- move beyond toy datasets
- build a project recruiters actually respect
Most ML beginners never touch text classification for operations — this gives you an edge.
Showcase Your Work
Once completed:
- Share screenshots or demo videos on LinkedIn
- Explain which business you built it for
- Tag Future Interns
- https://www.linkedin.com/company/future-interns/
This builds visibility, confidence, and credibility.