Cutoff Trend Prediction for Engineering Admis-sions Using Machine Learning

Engineering admission in Maharashtra is carried out through merit-based Centralised Admission Process (CAP) rounds, in which every college, branch and reservation category closes at a different cutoff percentile each year. Students usually rely on the previous year's cutoff list to decide their preference order, but cutoffs move from year to year because of changes in seat matrices, examination difficulty, branch demand and category-wise competition. Existing college-predictor tools mostly compare a candidate's score with last year's closing val-ues and rarely model how the cutoff itself is changing. This paper reviews five selected studies on admission prediction, cutoff-based college prediction and time-series forecasting, and iden-tifies gaps in temporal modelling, validation and uncertainty reporting. Based on these findings, a Machine Learning Framework for Cutoff Trend Prediction is proposed. The framework con-verts official CAP cutoff lists into a structured dataset of about 239,840 records, derives lag-based and trend features for every college-branch-category-round combination, and trains re-gression models such as Ridge regression, Random Forest and gradient boosting, with LSTM and ARIMA considered as optional time-series baselines. The forecast cutoff for the coming year is compared with a student's percentile to group colleges as Safe, Target or Dream. A pro-totype web application with search, prediction and history modules has been developed to de-liver the results. The framework aims to improve the accuracy, transparency and practical use-fulness of cutoff information for engineering aspirants. Quantitative evaluation of the proposed models is identified as future work.