Vol. 1 No. 04 (2024): Predictive Modeling for Undergraduate Engineering Branch Allocation Leveraging Machine Learning to Optimize Admissions

The allocation of branches in the admission process of undergraduate engineering programs plays a crucial role in shaping the academic journey of students. With limited seats and diverse preferences among applicants, accurately predicting the branch allocation based on ranks becomes imperative for educational institutions. This project aims to develop a predictive model for branch allocation, leveraging historical data and machine learning techniques. By analyzing past admission trends, the project seeks to identify patterns and factors influencing branch preferences. Utilizing algorithms such as regression analysis and decision trees, the model will forecast the likelihood of a student being allocated to a specific branch based on their rank and other relevant parameters. The project will also explore the integration of student preferences and institutional requirements to enhance the accuracy of predictions. Ultimately, the proposed predictive model aims to assist admission committees in making informed decisions, optimizing branch allocation, and ensuring a fair and efficient admission process for aspiring engineering students.