Objective
Build an NLP-powered system that automatically classifies and prioritizes network trouble tickets based on their text description, reducing manual triage effort and improving response times.
Business Value
For IT Service Desk:- Automated Triage: Eliminate manual ticket classification and routing delays
- Response Time Improvement: Accelerate high-priority issue resolution through intelligent prioritization
- Resource Optimization: Assign specialized technicians to appropriate ticket categories
- SLA Compliance: Meet service level agreements through predictive priority assignment
For Network Operations:- Incident Management: Streamline network incident response workflows
- Knowledge Management: Extract insights from historical ticket patterns
- Capacity Planning: Predict staffing needs based on ticket volume and complexity
- Customer Satisfaction: Reduce resolution times through efficient ticket handling
Core Libraries
- pandas & numpy: Ticket data processing and text analysis
- scikit-learn: TF-IDF vectorization and Logistic Regression for multi-class classification
- matplotlib & seaborn: Ticket pattern analysis and classification performance visualization
- nltk/spacy: Advanced text preprocessing and feature extraction
Dataset
Source: Synthetically Generated Network Trouble Tickets- Categories: Connectivity, Performance, Security, Hardware, Configuration issues
- Priority Levels: P1 (Critical), P2 (High), P3 (Medium), P4 (Low)
- Ticket Text: Realistic problem descriptions with technical terminology
- Metadata: Creation time, affected systems, user impact levels
Ticket Categories:- Connectivity: Network outages, routing issues, link failures
- Performance: Bandwidth problems, latency issues, throughput degradation
- Security: Intrusion alerts, policy violations, access issues
- Hardware: Equipment failures, port problems, power issues
- Configuration: Settings errors, policy misconfigurations, change issues
Step-by-Step Guide
1. Synthetic Ticket Dataset Generation
# Create realistic network trouble tickets
templates = {
'Connectivity': {
'P1': "Total network outage in {location}. All users affected.",
'P2': "Intermittent connectivity to {system}. Multiple users impacted.",
'P3': "Single user cannot access {resource}. Local issue suspected.",
'P4': "Scheduled maintenance required for {equipment}."
}
}
2. Text Preprocessing and Feature Engineering
# Clean and prepare ticket text for analysis
def preprocess_ticket_text(text):
text = text.lower()
text = re.sub(r'[^a-zA-Z\s]', '', text)
text = ' '.join(text.split())
return text
# Apply preprocessing to all tickets
df['processed_text'] = df['description'].apply(preprocess_ticket_text)
3. TF-IDF Vectorization
# Convert text to numerical features
vectorizer = TfidfVectorizer(
max_features=1000,
stop_words='english',
ngram_range=(1, 2) # Include bigrams for better context
)
X_tfidf = vectorizer.fit_transform(df['processed_text'])
4. Multi-class Classification Models
# Separate models for category and priority prediction
category_model = LogisticRegression(
multi_class='ovr',
class_weight='balanced',
random_state=42
)
priority_model = LogisticRegression(
multi_class='ovr',
class_weight='balanced',
random_state=42
)
5. Model Training and Evaluation
# Train both classification models
category_model.fit(X_train_tfidf, y_train_category)
priority_model.fit(X_train_tfidf, y_train_priority)
# Evaluate performance
category_pred = category_model.predict(X_test_tfidf)
priority_pred = priority_model.predict(X_test_tfidf)
6. Feature Importance Analysis
# Identify most important terms for each category
feature_names = vectorizer.get_feature_names_out()
for category, coef in zip(categories, category_model.coef_):
top_features = feature_names[coef.argsort()[-10:]]
print(f"{category}: {top_features}")
Success Criteria
Primary Metrics:- Category Classification Accuracy: >90% for ticket routing
- Priority Classification Accuracy: >85% for SLA compliance
- Macro F1-Score: >0.85 across all classes for balanced performance
Secondary Metrics:- Processing Speed: <100ms per ticket for real-time classification
- Model Interpretability: Clear feature weights for business understanding
- Confidence Scores: Probability estimates for manual review thresholds
Business Impact:- Reduce manual triage time by 80%
- Improve P1 incident response time by 50%
- Increase technician utilization through better routing
- Achieve 95% SLA compliance through accurate prioritization
Next Steps & Extensions
Immediate Improvements
- Active Learning: Incorporate analyst feedback to improve model accuracy
- Multi-label Classification: Handle tickets with multiple categories
- Sentiment Analysis: Detect customer frustration levels in ticket text
Advanced NLP Techniques
- BERT/Transformer Models: Use pre-trained language models for better understanding
- Named Entity Recognition: Extract network components, locations, and systems
- Topic Modeling: Discover hidden patterns in ticket descriptions
- Text Similarity: Find related tickets for knowledge base suggestions
Production Integration
- Real-time API: Deploy models as REST services for ticket system integration
- Confidence Thresholds: Route uncertain predictions to human reviewers
- Model Monitoring: Track prediction accuracy and retrain as needed
- A/B Testing: Compare automated vs manual triage performance
Specialized Features
- Time-aware Models: Consider time of day, day of week patterns
- User Profiling: Incorporate requester history and expertise levels
- System Integration: Connect with monitoring tools for automated ticket creation
- Escalation Logic: Implement smart escalation based on resolution time predictions
Analytics and Reporting
- Trend Analysis: Identify recurring issues and root causes
- Performance Dashboards: Track triage accuracy and processing metrics
- Capacity Forecasting: Predict ticket volumes and resource requirements
- Knowledge Mining: Extract solutions from resolved tickets for knowledge base
This project transforms IT service desk operations by applying advanced NLP to automate the most time-consuming aspects of ticket management, enabling faster incident resolution and improved customer satisfaction.