🎫 AI-Powered Support Ticket Intelligence System

🤖 Enterprise NLP & Machine Learning Solution for Smart Customer Support Automation

---

🚀 Project Overview

Modern businesses receive thousands of customer support tickets every day.

These include:

• 💳 Billing Issues
• 🐛 Technical Problems
• 👤 Account Issues
• 🔒 Security Concerns
• 📞 General Queries

Manual ticket handling creates major operational challenges:

❌ Tickets are not categorized properly ❌ Urgent issues get delayed ❌ Support teams waste time sorting tickets ❌ Customer satisfaction decreases

---

🎯 Solution

This project uses:

✅ Natural Language Processing (NLP) ✅ Machine Learning ✅ AI-driven operational intelligence

to automatically:

• classify tickets
• prioritize issues
• analyze sentiment
• detect operational risk
• route tickets intelligently
• generate AI-powered support reports

---

✨ Features

🎯 Intelligent Ticket Classification

Automatically classifies tickets into:

• 💰 Billing
• 💻 Technical Support
• 👤 Account Management
• 🔒 Security Issues
• 📞 General Support

---

⚡ Priority Prediction

Predicts:

• 🔴 Critical
• 🟠 High
• 🟡 Medium
• 🟢 Low

priority tickets automatically.

---

😊 Sentiment & Emotion Analysis

Analyzes:

• Very Negative
• Negative
• Neutral
• Positive
• Very Positive

and emotions like:

• Angry
• Frustrated
• Happy
• Dissatisfied

---

🛡️ AI Risk Assessment

Detects:

• Fraud Risk
• Security Breach Risk
• Financial Impact
• Customer Churn Risk

with intelligent risk scoring.

---

📨 Smart Ticket Routing

Automatically routes tickets to:

Category	Assigned Team
💰 Billing	Finance Team
💻 Technical	Technical Support
👤 Account	Account Management
🔒 Security	Security Team

---

📊 AI Decision Support System

Generates:

• AI operational reports
• urgency score
• escalation level
• SLA prediction
• business impact
• AI summaries
• suggested resolutions

---

📈 Interactive Analytics Dashboard

Includes:

• 📊 Ticket distribution charts
• 📉 Confusion matrix
• 📌 Support workload analytics
• ☁️ WordCloud visualization
• 📋 Model performance metrics
• 💰 ROI calculator

---

🛠️ Tech Stack

👨‍💻 Programming

• Python

🤖 Machine Learning

• Scikit-learn
• Logistic Regression
• TF-IDF Vectorization

🧠 NLP

• TextBlob
• NLTK

📊 Visualization

• Matplotlib
• Seaborn
• WordCloud

🌐 Frontend

• Streamlit

---

📁 Project Structure

support-ticket-classification/
│
├── data/
│   ├── raw/
│   └── processed/
│
├── notebooks/
│   ├── 01_data_understanding.ipynb
│   ├── 02_text_preprocessing.ipynb
│   ├── 03_feature_engineering.ipynb
│   ├── 04_ticket_classification.ipynb
│   ├── 05_priority_prediction.ipynb
│   └── 06_model_evaluation.ipynb
│
├── models/
│   ├── ticket_classifier.pkl
│   ├── tfidf_vectorizer.pkl
│   └── label_encoder.pkl
│
├── outputs/
│
├── app.py
├── requirements.txt
└── README.md

---

🔄 Complete Workflow

Customer Support Ticket
            ↓
Text Cleaning & Preprocessing
            ↓
Tokenization & NLP Processing
            ↓
TF-IDF Feature Extraction
            ↓
Machine Learning Classification
            ↓
Priority Prediction
            ↓
Sentiment & Risk Analysis
            ↓
AI Operational Intelligence
            ↓
Smart Routing & SLA Prediction
            ↓
Comprehensive AI Support Report

---

📊 Machine Learning Pipeline

🔹 Step 1 — Data Collection

Load support ticket dataset.

---

🔹 Step 2 — Text Preprocessing

Perform:

• lowercasing
• punctuation removal
• stopword removal
• tokenization

---

🔹 Step 3 — Feature Engineering

Convert text into numerical vectors using:

• TF-IDF Vectorization

---

🔹 Step 4 — Model Training

Train:

• Logistic Regression classifier

---

🔹 Step 5 — Model Evaluation

Evaluate using:

• Accuracy
• Precision
• Recall
• F1-score
• Confusion Matrix

---

🔹 Step 6 — AI Operational Analysis

Perform:

• sentiment analysis
• risk assessment
• urgency detection
• smart routing

---

📈 Model Evaluation Metrics

The project includes:

✅ Accuracy Score ✅ Precision Score ✅ Recall Score ✅ F1 Score ✅ Confusion Matrix ✅ Classification Report

---

▶️ How To Run The Project

1️⃣ Clone Repository

git clone <your-github-repository-link>

---

2️⃣ Open Project Folder

cd support-ticket-classification

---

3️⃣ Create Virtual Environment

Windows

python -m venv venv

Activate environment:

venv\Scripts\activate

---

4️⃣ Install Dependencies

pip install -r requirements.txt

---

5️⃣ Install Streamlit

pip install streamlit

---

6️⃣ Run Jupyter Notebooks

Run notebooks in order:

Notebook	Purpose
01_data_understanding.ipynb	Dataset exploration
02_text_preprocessing.ipynb	NLP preprocessing
03_feature_engineering.ipynb	TF-IDF vectorization
04_ticket_classification.ipynb	ML training
05_priority_prediction.ipynb	Priority logic
06_model_evaluation.ipynb	Performance evaluation

This generates:

• trained models
• vectorizers
• processed data

---

7️⃣ Run Streamlit Application

streamlit run app.py

---

8️⃣ Open Browser

Streamlit automatically opens:

http://localhost:8501

---

💡 Example Ticket

My payment failed twice and money was deducted from my account.
Please resolve this urgently.

---

📋 Example AI Report

Feature	Output
Ticket Category	Billing Issue
Priority	High
Sentiment	Very Negative
Risk Level	High Risk
Assigned Team	Finance Team
SLA Target	2 Hours
Urgency Score	92%
Suggested Resolution	Verify payment gateway logs

---

💰 Business Impact

This AI system helps businesses:

✅ Reduce manual ticket sorting ✅ Improve customer response time ✅ Detect urgent issues faster ✅ Reduce SLA violations ✅ Improve operational efficiency ✅ Reduce support costs ✅ Improve customer satisfaction

---

📌 ROI Benefits

Organizations can:

• reduce support handling time
• optimize support workforce
• reduce operational costs
• improve customer retention

---

🚀 Future Enhancements

• Deep Learning Models
• Transformer-based NLP
• Real-time Database Integration
• Multi-language Support
• Voice-based Ticket Analysis
• Cloud Deployment
• Generative AI Auto-Reply System

---

👨‍💻 Author

Subhankar Nandi

🎓 BTech CSE AIML Student 🤖 Machine Learning & AI Enthusiast 🚀 Passionate about NLP, AI Automation & Intelligent Systems

---