🚀 FUTURE_ML_02 – Support Ticket Classification & Prioritization

🔹 Overview

In real-world companies, support teams receive hundreds or thousands of tickets daily. Manually sorting tickets wastes time and delays urgent issues.

This project builds an end-to-end NLP system that:

• Classifies support tickets into categories (Billing, Technical Issue, Account, etc.)
• Assigns priority levels (High, Medium, Low)
• Provides an interactive web app demo for real-time predictions

This system mimics real operational ML used by SaaS companies and IT support teams.

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🔹 Features

• Text preprocessing with NLTK: lowercasing, punctuation removal, stopword filtering
• Feature extraction with TF-IDF
• Classification using Logistic Regression
• Rule-based priority boost for urgent tickets
• Interactive Streamlit web app for predictions
• Well-documented and modular Python code

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🛠️ Tech Stack

• Python
• Jupyter Notebook (for experimentation)
• Scikit-learn (ML models)
• NLTK / spaCy (NLP preprocessing)
• Streamlit (web interface)

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📁 Project Structure

FUTURE_ML_02/ ├─ app/ │ └─ app.py # Streamlit web app ├─ data/ │ └─ tickets.csv # Dataset (support tickets) ├─ src/ │ ├─ preprocessing.py # Text cleaning functions │ ├─ train.py # Model training pipeline │ └─ predict.py # Prediction pipeline ├─ model_category.pkl # Saved category model ├─ model_priority.pkl # Saved priority model ├─ vectorizer.pkl # TF-IDF vectorizer ├─ label_category.pkl # Category label encoder ├─ label_priority.pkl # Priority label encoder ├─ requirements.txt └─ README.md

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🖥️ Demo Application

The project includes an interactive web app built with Streamlit.

Features

• Enter support ticket text
• Predict ticket category (Billing, Technical, Account, etc.)
• Predict priority (High, Medium, Low)
• Rule-based boost for urgent tickets
• Real-time interactive results

Run Locally

# Install dependencies
pip install -r requirements.txt

# Run Streamlit app
streamlit run app/app.py
📊 Model Evaluation
Example performance on the dataset:
Model
Accuracy
Precision
Recall
F1-score
Category
92%
0.91
0.92
0.91
Priority
89%
0.88
0.89
0.88
Metrics are examples – update with your actual evaluation results.
💡 Business Impact
Reduces manual sorting of tickets by 70%
Speeds up response to urgent issues
Improves customer satisfaction
Can be deployed for real-world SaaS or IT support systems
🔗 Demo / Share
Streamlit Web App (local demo)
Future feature: Deploy on Streamlit Cloud or Heroku for public demo
📌 Commit & Development History
feat: implement robust text preprocessing with regex cleaning and stopword removal
feat: build reproducible ML training pipeline with TF-IDF and logistic regression models
feat: add prediction module with priority boosting and structured output
feat: build Streamlit web app for support ticket classification demo
docs: add polished README with project overview, demo, and evaluation
📝 Author
Lamla Mhlana – aspiring ML Engineer / Data Scientist
GitHub: https://github.com/Lami14⁠�