Abstract :

The Process Of Graduate School Admission Is Complex And Influenced By Multiple Academic And Non-academic Factors Such As GRE Scores, GPA, University Ranking, Statement Of Purpose, Letters Of Recommendation, And Research Experience. Students Often Face Uncertainty In Predicting Their Chances Of Admission, Leading To Difficulty In Decision-making And Application Planning. This Project Aims To Demystify And Anticipate Graduate School Admissions Using Machine Learning Algorithms By Providing A Data-driven Approach To Estimate Admission Probabilities. The Proposed System Utilizes Historical Admission Datasets Containing Various Applicant Features And Admission Outcomes. Data Preprocessing Techniques Such As Normalization, Handling Missing Values, And Feature Selection Are Applied To Improve Data Quality. Machine Learning Algorithms Including Linear Regression, Decision Trees, Random Forest, And Support Vector Machines Are Implemented To Model The Relationship Between Applicant Profiles And Admission Chances. The Dataset Is Split Into Training And Testing Sets In An 80:20 Ratio To Evaluate Model Performance. The System Predicts The Probability Of Admission For New Applicants Based On Their Input Parameters. Performance Is Evaluated Using Metrics Such As Accuracy, Mean Squared Error, And R-squared Values. Experimental Results Show That Ensemble Methods Like Random Forest Provide Better Prediction Accuracy Compared To Other Models. This Approach Helps Students Make Informed Decisions Regarding University Selection And Application Strategies, Reducing Uncertainty And Improving Planning.

Published:

08-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1779-1785

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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