Automated University Complaint Management System by Leveraging Machine Learning and Natural Language Processing for Enhanced Efficiency and Accuracy

The goal of this research is to create an automated system that works with machine learning (ML) and natural language processing (NLP) to automate university complaint management. Students get dissatisfied when traditional complaint handling techniques, such physical suggestion boxes, are ineffective and prone to delays. Designing and implementing a system that automates the submission, classification, and analysis of student complaints especially those made in Somali is the aim of this project. The suggested approach greatly lessens the manual workload of university administrators by classifying issues into Academic, Finance, and Equipment using a machine learning model trained on complaint data. The system has an administrative dashboard for tracking and handling complaints, as well as an easy-to-use interface for filing complaints. The primary results show that the system improves the accuracy and efficiency of resolving complaints, which results in quicker resolution times and pleased students. Proactive decision-making is made possible by the system’s integration of data analytics, which also offers insightful information on persistent problems. According to the project’s findings, automated complaint handling can greatly enhance the entire university experience by creating a more accommodating and student-focused atmosphere.