College Admission Prediction System

Abstract

Choosing the right college and branch during the admission process is one of the toughest decisions for students. Many are unsure about which colleges they qualify for, their chances of getting into a specific department, and whether a college will offer good placements and career opportunities. This project, College Admission Prediction System, aims to help students make informed choices by predicting possible college options and providing useful insights.

The system works in several steps. First, students enter their academic details, such as PCM marks, to check their eligibility for engineering programs. Once eligibility is confirmed, students provide their percentile score. Based on this score, the system compares the student’s data with the last two to three years of CAP round seat allotment records to predict a list of colleges in Sangli District where the student may have a chance of admission. It then shows key details like college name, institute code, address, available departments, cutoff ranges, and the likelihood of gaining admission in each branch.

The project GUI is developed using HTML, CSS, and Django, scikit-learn implements machine learning models for cutoff prediction, and MySQL manages the database. With additional insights like placement statistics, this system not only predicts admission chances but also assists students in making more confident, data-driven choices about their college preferences.

KEYWORDS: College Admission Prediction, Machine Learning, Django Framework, MySQL Database, Data Preprocessing