Web Tutor Finder Using Machine Learning (case Study of Hnd Computer Science, Federal Polytechnic, Mubi)

-based tutor finder systems present a significant advancement in educational technology, enhancing the process of connecting students with suitable tutors. This study explores the development and implementation of an ML-enhanced tutor finder system specifically designed for the HND Computer Science Department. By leveraging PHP and MySQL for system development, and incorporating advanced ML algorithms, the platform aims to improve the accuracy and efficiency of tutor-student matching. Key features of the system include a robust login interface, comprehensive admin dashboard, intuitive registration interface, dynamic tutor and course list interfaces, detailed course information, and efficient communication channels. The study highlights the benefits of ML, such as improved personalization, increased efficiency, data-driven insights, and scalability, while also addressing challenges related to data quality, privacy, complexity, and bias. Recommendations for further improvements and potential areas for future research are discussed, emphasizing the need for continued innovation and user-centric design in educational technologies. This work contributes to the field by providing a practical framework for the integration of ML in tutor finder systems, offering valuable insights for other academic institutions seeking to enhance their educational support services.