POSTGRADUATE ADMISSION PREDICTION USING MACHINE LEARNING

The competition for postgraduate admissions has intensified due to a rise in the number of applicants. Manystudents struggle to understand specific admission criteria and rely on costly and biased consultancy services. Toaddress this, a machine learning-based system is proposed to predict admission chances based on individualprofiles. The system uses a historical dataset with features like GRE scores, GPA, TOEFL Scores, Statement ofpurpose, Letter of recommendation, research experience, and professional background. Three models aredeveloped and compared: Linear Regression, Decision Tree Regression, and Logistic Regression. Linear andDecision Tree models explore linear and non-linear patterns, respectively. Logistic Regression, designed forbinary classification, predicts admission probabilities effectively. Logistic Regression shows superior accuracyand minimal error, making it the best choice. The system provides a scalable and data-driven alternative to helpstudents apply strategically.