Maintenance Tracker and Scheduler

Abstract

"The Maintenance Tracker System project aims to tackle the difficulties posed by the existing manual maintenance reporting methods in apartment complexes. The obstacles include arduous reporting processes, inadequate communication and openness, and delays in resolving issues. The project seeks to enhance productivity and customer satisfaction by implementing a centralized digital platform that facilitates the reporting and monitoring of maintenance concerns. The technique used in this project is developing a web application that is capable of adapting to different devices and allows users to file complaints on maintenance issues. Subsequently, a machine learning model is used to examine these complaints and identify their level of urgency. The strategy emphasizes the use of a user-friendly interface to make it easier for users to submit complaints. It also use machine learning algorithms to prioritize concerns in a way that is both efficient and accurate. Initial tests of the Maintenance Tracker System show that it can guess the level of importance of maintenance issues with a 0.57 accuracy. Even though it's pretty accurate, it might take more research and model tuning to make it work better, especially when it comes to the priority level"