🎫 Cloud-Based Intelligent Support Ticket Classification Platform

🚀 Live Demo • 📖 API Docs

A production-grade AI/ML system that automatically classifies, prioritizes, and analyzes customer support tickets using supervised machine learning. Built for enterprise scalability and deployed via Docker on AWS.

📋 Table of Contents

• Business Impact
• Features
• Tech Stack
• Quick Start
• Project Structure
• API Documentation
• Model Details
• Deployment
• Testing
• Contributing

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💼 Business Impact

The Problem

Customer support teams face overwhelming ticket volumes, leading to:

• ⏰ Slow response times from manual triage
• 🎯 Misrouted tickets causing customer frustration
• 📉 Inconsistent prioritization missing critical issues
• 💰 High labor costs for manual classification

The Solution

This platform provides instant, AI-powered ticket classification that:

• ⚡ Reduces triage time by 90% - from minutes to milliseconds
• 🎯 Achieves 85%+ accuracy in category prediction
• 🔄 Enables automatic routing to specialized teams
• 📊 Provides confidence scores for human-in-the-loop workflows
• 💵 Cuts operational costs by automating repetitive tasks

ROI Example

For a support team handling 1,000 tickets/day:

Metric	Before	After	Impact
Avg. Triage Time	2 min	0.1 sec	99.9% faster
Mis-routing Rate	25%	5%	80% reduction
Agent Efficiency	50 tickets/day	75 tickets/day	50% increase

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✨ Features

Core Capabilities

• 🏷️ Category Classification: Billing, Technical, Account, Feature Request, General Inquiry
• ⚡ Priority Prediction: Critical, High, Medium, Low
• 📊 Confidence Scores: Probability distribution for all classes
• 🔄 Batch Processing: Classify multiple tickets in one API call
• 📈 Real-time Metrics: Model performance monitoring

Technical Features

• 🔌 RESTful API with OpenAPI/Swagger documentation
• 🐳 Docker containerized for consistent deployments
• ☁️ AWS-ready with EC2/ECS deployment guides
• 🧪 Comprehensive testing with pytest
• 📊 Visualization suite for data analysis

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🛠️ Tech Stack

Category	Technology
ML/Data Science	scikit-learn, Pandas, NumPy, NLTK
API Framework	FastAPI, Uvicorn, Pydantic
Database	SQLite (dev), PostgreSQL (prod-ready)
Visualization	Matplotlib, Seaborn
Containerization	Docker, Docker Compose
Cloud	AWS EC2, ECS, ECR
Testing	pytest, httpx

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🚀 Quick Start

Prerequisites

• Python 3.11+
• pip or conda
• Docker (optional, for containerized deployment)

Installation

# Clone the repository
git clone https://github.com/yourusername/ticket-classifier.git
cd ticket-classifier

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Download NLTK data
python -c "import nltk; nltk.download('punkt'); nltk.download('punkt_tab'); nltk.download('stopwords'); nltk.download('wordnet')"

Train the Model

# Run the training pipeline
python scripts/train.py

This will:

1. Generate 500 synthetic tickets
2. Store them in SQLite database
3. Preprocess text with NLP pipeline
4. Train TF-IDF + Logistic Regression model
5. Generate visualizations
6. Save the trained model

Start the API

# Development mode with hot reload
uvicorn src.api.main:app --reload --port 8000

# Production mode
uvicorn src.api.main:app --host 0.0.0.0 --port 8000

Test the API

# Health check
curl http://localhost:8000/health

# Predict ticket category
curl -X POST http://localhost:8000/predict \
  -H "Content-Type: application/json" \
  -d '{
    "subject": "Cannot login to my account",
    "description": "I have been trying to login for an hour but keep getting invalid credentials error"
  }'

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📁 Project Structure

ticket-classifier/
├── src/
│   ├── api/                    # FastAPI application
│   │   ├── main.py             # API endpoints
│   │   └── schemas.py          # Pydantic models
│   ├── data/                   # Data processing
│   │   ├── generator.py        # Synthetic data generation
│   │   ├── database.py         # SQLite operations
│   │   └── preprocessing.py    # NLP preprocessing
│   ├── models/                 # ML models
│   │   ├── classifier.py       # TF-IDF + LogReg classifier
│   │   └── serialized/         # Saved model files
│   └── visualizations/         # Matplotlib plots
│       └── plots.py
├── data/                       # Dataset storage
├── scripts/                    # Utility scripts
│   └── train.py                # Training pipeline
├── tests/                      # Test suite
├── docs/                       # Documentation
│   └── deployment.md           # AWS deployment guide
├── Dockerfile
├── docker-compose.yml
├── requirements.txt
└── README.md

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📖 API Documentation

Interactive Docs

Once running, visit:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Endpoints

Method	Endpoint	Description
GET	/health	Health check
GET	/metrics	Model performance metrics
POST	/predict	Classify single ticket
POST	/predict/batch	Classify multiple tickets
GET	/categories	List all categories
GET	/priorities	List all priorities

Example Response

{
  "ticket_text": "Cannot login to my account I have been trying...",
  "category": "Account",
  "priority": "High",
  "confidence_category": 0.847,
  "confidence_priority": 0.623,
  "category_probabilities": {
    "Account": 0.847,
    "Technical": 0.089,
    "Billing": 0.032,
    "General Inquiry": 0.021,
    "Feature Request": 0.011
  },
  "priority_probabilities": {
    "High": 0.623,
    "Medium": 0.241,
    "Critical": 0.098,
    "Low": 0.038
  }
}

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🧠 Model Details

Why Supervised Learning?

We use supervised learning because:

1. Labeled data available: Historical tickets have known categories and priorities
2. Clear class definitions: 5 categories and 4 priority levels are well-defined
3. Interpretability: Logistic Regression coefficients show which words influence predictions
4. Production-ready: Fast inference (<10ms) suitable for real-time API

Algorithm: TF-IDF + Logistic Regression

Component	Purpose	Configuration
TF-IDF Vectorizer	Convert text to numerical features	max_features=5000, ngram_range=(1,2)
Logistic Regression	Multi-class classification	C=1.0, class_weight='balanced'

Preprocessing Pipeline

1. Lowercase: Normalize case
2. URL/Email Removal: Strip non-content elements
3. Tokenization: Split into words (NLTK)
4. Stopword Removal: Remove common words + domain-specific terms
5. Lemmatization: Reduce words to base form (WordNet)
6. Length Filter: Remove tokens < 2 characters

Performance Metrics

Target	Accuracy	Precision	Recall	F1 Score
Category	~85%	~84%	~85%	~84%
Priority	~70%	~68%	~70%	~68%

Note: Priority prediction is harder due to subjective nature of urgency assessment.

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☁️ Deployment

Docker Deployment

# Build image
docker build -t ticket-classifier .

# Run container
docker run -d -p 8000:8000 --name ticket-api ticket-classifier

# Or use docker-compose
docker-compose up -d

AWS Deployment

See docs/deployment.md for detailed guides on:

• EC2: Traditional VM deployment
• ECS Fargate: Serverless container deployment
• Security: SSL/TLS, security groups
• Monitoring: CloudWatch integration

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🧪 Testing

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=src --cov-report=html

# Run specific test file
pytest tests/test_api.py -v

Test Coverage

• test_preprocessing.py: Text cleaning, tokenization, stopwords
• test_classifier.py: Model training, prediction, serialization
• test_api.py: API endpoints, validation, error handling

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📊 Visualizations

After training, check the visualizations/ directory for:

• category_distribution.png: Bar chart of ticket categories
• priority_distribution.png: Pie chart of priority levels
• confusion_matrix_category.png: Category prediction accuracy
• confusion_matrix_priority.png: Priority prediction accuracy
• metrics_comparison.png: Model performance metrics

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🔮 Future Enhancements

• BERT/Transformer models for improved accuracy
• Auto-response generation based on category
• Sentiment analysis integration
• Multi-language support
• Active learning for model improvement
• Real-time model retraining pipeline

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🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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👤 Author

Ayan Chatterjee

• LinkedIn: Ayan Chatterjee

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Made with ❤️ by @Ayan Chatterjee using Python, FastAPI, and scikit-learn