Abstract

The increasing complexity of IT service management underscores the need for efficient and scalable solutions to manage IT tickets. This thesis explores the application of machine learning techniques for automated prioritization & routing of IT tickets. So by leveraging publicly available dataset from Kaggle containing IT ticket the study aims to reduce manual intervention, improve resolution times, & optimize resource allocation within IT environments. The study will evaluate various machine learning algorithms, including SVM, Random Forest, & ensemble models, for their ability to classify and prioritize tickets accurately. to accurately classify and prioritize tickets. Advanced data preprocessing techniques like TF-IDF vectorization and class balancing are used to handle data inconsistencies and imbalances. Additionally, the study also explores hybrid approaches that combine machine learning with rule-based systems are investigated to enhance classification performance for low-frequency and ambiguous ticket categories. Moreover, incorporating feedback loops & real-time data updates ensures model adaptability to evolving IT environments. The expected outcomes include significant improvements in classification accuracy and the development of a scalable framework for real-time IT ticket management. By using a publicly available dataset, this research aims to provide a framework for organizations looking to enhance service efficiency and comply service-level agreements standards.

Library of Congress Subject Headings

Data processing service centers--Automation; Issues management--Data processing; Project management--Data processing; Machine learning

Publication Date

1-2025

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ehsan Warriach

Campus

RIT Dubai

Plan Codes

PROFST-MS

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