Published Paper Details:

TicketEase is a web-based platform meticulously engineered to streamline issue management processes within organizational contexts. The system employs advanced Machine Learning (ML) techniques to prioritize and classify employee complaints and issues, effectively addressing software, hardware, and application-related challenges. Leveraging Angular for frontend development, TicketEase ensures an intuitive user experience, while its backend infrastructure, powered by Spring Boot, Java, and Hibernate, provides robustness and scalability. Technologies such as Maven, GitHub, Postman, Swaggy, and MySQL are seamlessly integrated to facilitate efficient development, testing, documentation, and database management. By employing Random Forest, TicketEase enhances its ability to accurately categorize and prioritize incoming issues, ensuring that urgent and critical matters receive prompt attention and resolution. The algorithm's robustness and scalability contribute to the platform's effectiveness in managing a large volume of tickets while maintaining high levels of accuracy and efficiency. The platform's architecture and methodologies are meticulously designed to adapt to evolving organizational needs and technological advancements. This paper offers a comprehensive exploration of TicketEase, detailing its architecture, ML algorithms, and integration of key technologies. Through experimentation and evaluation, TicketEase demonstrates its efficacy in efficiently managing issues, offering organizations a potent tool to bolster operational efficiency and foster a culture of continuous improvement. As organizations navigate the complexities of modern work environments, TicketEase emerges as a transformative solution, poised to revolutionize issue management practices and drive organizational success.