College Suggesta: Enhancing Choices with K-Means, KNN, and Cosine Similarity Using Flutter and Django
Dasari Chinna Veeraiah, Ch. Anuradha, Palli Sai Nithin, Peruri Aruna Kumari · 2024
College admission is one of the most crucial stages of a student’s life. A wrong choice can greatly affect the future career prospects of a student. Choosing the right college helps the student gain advanced knowledge that they can learn with passion and interest. It boosts their ability to abstract intellectual concepts. Students consider various factors while choosing a college, such as region, infrastructure, library resources, ranking, placements and internship opportunities, tuition fees, and hostel availability. we developed a recommendation system leveraging K-Means clustering and K-Nearest Neighbors (KNN) algorithms. The system utilizes a dataset with attributes such as college rankings, placements, fees, and accreditation statuses. K-Means clustering groups similar colleges, while KNN refines these clusters based on user preferences, providing personalized recommendations. This approach effectively aligns college choices with individual preferences, enhancing the decision-making process. The system is available as a mobile application, offering detailed insights on various colleges, including placement records and infrastructure quality, developed using Flutter for the front end and Django restframe work for the back end.