Papers by Kukkara Vijaya Lakshmi Prasanna
IJETRM, 2025
The competition for postgraduate admissions has intensified due to a rise in the number of applic... more The competition for postgraduate admissions has intensified due to a rise in the number of applicants. Many students struggle to understand specific admission criteria and rely on costly and biased consultancy services. To address this, a machine learning-based system is proposed to predict admission chances based on individual profiles. The system uses a historical dataset with features like GRE scores, GPA, TOEFL Scores, Statement of purpose, Letter of recommendation, research experience, and professional background. Three models are developed and compared: Linear Regression, Decision Tree Regression, and Logistic Regression. Linear and Decision Tree models explore linear and non-linear patterns, respectively. Logistic Regression, designed for binary classification, predicts admission probabilities effectively. Logistic Regression shows superior accuracy and minimal error, making it the best choice. The system provides a scalable and data-driven alternative to help students apply strategically.
Uploads
Papers by Kukkara Vijaya Lakshmi Prasanna