Programme
HDIP in Data Analytics for Business
Abstract
This capstone project investigates the application of machine learning and natural language processing (NLP) to enhance customer support operations through automated ticket classification, prioritization, and summarization. Using the multilingual Customer Support Emails dataset from Kaggle, the project follows the CRISP-DM methodology, performing extensive data cleaning, preprocessing, feature engineering, and class balancing. Five machine learning models—Decision Tree, KNN, LinearSVC, Naive Bayes, and Random Forest—were evaluated using hyperparameter tuning, cross-validation, confusion matrix analysis, and learning curves. LinearSVC demonstrated the strongest performance for both queue and priority classification, achieving accuracies of 89.8% and 81.2% respectively, with consistent generalization across folds. For summarization, extractive and abstractive methods were implemented using BERT and BART, with extractive summarization selected as the most reliable for preserving technical accuracy. The results show that machine learning can significantly improve ticket routing efficiency, reduce resolution time, and support customer service agents by providing concise issue overviews. This work demonstrates a practical framework for integrating AI into customer support workflows while addressing ethical considerations such as data privacy, fairness, and robustness.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Nguyen, H. N. (2025) Customer Service Support. Utilizing machine learning to classify, prioritize and summarize issues. CCT College Dublin.