Post Graduation Admission Prediction Using Machine Learning
Post Graduation Admission Prediction Using Machine Learning
Tulasi Krishna Kumar1, Viyyapu Aruna2
1 Associate Professor & Training & Placement Officer
2 MCA Final Semester, Master of Computer Applications, Sanketika Vidya Parishad Engineering College, Vishakhapatnam,
Andhra Pradesh, India
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ABSTRACT
The Post Graduation Admission Prediction system is a machine learning-based project developed to predict student admission chances for postgraduate studies. The system uses factors such as GRE score, TOEFL score, CGPA, SOP, LOR, university rating, and research experience for prediction. Machine learning algorithms like Linear Regression and Random Forest are used to train the model. Based on the student’s input, the system predicts the probability of admission into universities. It also classifies universities into Tier 1, Tier 2, and Tier 3 categories. The project helps students make better decisions while applying for higher education. The system provides accurate and fast predictions through a simple user interface. This project reduces uncertainty and supports students in planning their academic future.
Keywords: Linear regression, Random forest, Decision tree, Logistic regression, Support vector machine(SVM), K-nearest Neighbours (KNN)