Scholarspot: Scholarship Recommendation System using Machine Learning

Scholarship applications continue to be difficult and time-consuming for students to complete. The current scholarship application processes are impersonal, which frequently causes mismatches between applicants' profiles and opportunities. Due to this inefficiency, deserving applicants miss out on opportunities, which adds to the problem of financial access to education as a whole. To simplify and improve the scholarship application process, this paper offers "ScholarSpot," a cutting-edge Scholarship Recommendation System. Scholar's Spot offers a personalized approach to scholarship suggestions by utilizing cutting-edge algorithms and data-driven insights. The technology generates customized suggestions based on user profiles, academic achievements, and personal goals, greatly accelerating the search for appropriate scholarships. The Scholar's Spot system uses cutting-edge algorithms and a user-centric approach to greatly enhance the matching of students with appropriate scholarships, resulting in saving a lot of time and effort of the students. With the help of different machine learning methods, for instance, SVM, DecisionTree, Random Forest, and content-based filtering, advertise its power and richness and enhance recommendations for customized scholarship. Despite a barely adequate accuracy score of 38%, similar to the one Support Vector Machine gave, Decision Tree algorithm outperformed the former, appearing to be at 72% accurate. Nevertheless, Random Forest was the algorithm that did so well and achieved a 78% correctness score, showing how effective it could have been in ensuring the accuracy of student-scholarship matching. Besides, choosing content-based filtration strategies provided an intelligent notice; thereby, the accuracy score was 94%, which shows that applying scholarship terms with applicant backgrounds can improve recommendation results. The findings are encouraging and show that the system can improve the scholarship application process and make it more suitable for students by tailoring the recommendations to their needs and providing a more precise information in accordance with what they are looking, contributing to their chances of acquiring a scholarship for their education.