The Scholarship Whizz seeks to enhance its platform by developing an advanced algorithm that efficiently matches students with scholarship opportunities tailored to their academic profiles, preferences, and eligibility criteria. The project challenges students to design and implement a sophisticated matching algorithm that provides personalized scholarship recommendations, thereby increasing user satisfaction and platform reach. This initiative offers students a practical opportunity to apply machine learning techniques to a real-world problem, focusing on the optimization of scholarship matching processes. The project is structured to be completed within 60 hours over a period of 2-8 weeks, allowing students to gain hands-on experience in algorithm development and data analysis. By the end of the project, students will have a deeper understanding of how machine learning can be leveraged to address challenges in educational technology.

Students will deliver a functional algorithm capable of accurately matching students with relevant scholarships based on provided data. The deliverables include a detailed report documenting the algorithm's design, implementation, and testing processes. Additionally, students will present a demonstration of the algorithm's performance, showcasing its ability to provide personalized scholarship recommendations. The final submission will also include annotated code and user documentation to facilitate future integration into The Scholarship Whizz platform.