EngiWave: Engineering College Recommendation System using Decision Tree and Random Forest Algorithm

Abstract

Students face numerous challenges when trying to enroll in their desired institution, particularly in engineering programs. The admissions process is complex, often resulting in students being accepted into lower-ranked institutions than they deserve. To address this, the Council Entrance Predictor utilizes historical acceptance data to forecast the most likely institutions for students. Engiwave, another tool, employs similar data to predict potential institutions based on academic performance, background, and admission criteria. These predictions are facilitated by a machine learning model trained on a dataset containing student profiles and university details, including admission outcomes. By leveraging machine learning and database techniques, the system provides students with a prioritized list of engineering institutions where they have the highest probability of admission, aiding them in preparing their applications effectively.