Predicting Graduate Admissions using Ensemble Machine Learning Techniques: A Comparative Study of Classifiers and Regressors
- Title
- Predicting Graduate Admissions using Ensemble Machine Learning Techniques: A Comparative Study of Classifiers and Regressors
- Creator
- Basha M.S.A.; Prabhavathi C.; Khangembam V.; Sucharitha M.M.; Oveis P.M.
- Description
- The goal of this research is to apply machine learning techniques to forecast a student's probability of being accepted into a graduate program. Applicants' GRE and TOEFL grades, university rankings, letters of recommendation, statements of purpose, cumulative grade point averages, and prior research experience are all included in the dataset utilized for this analysis. The goal is to calculate an applicant's expected acceptance rate. This study uses a combination of Classifiers and regressors. Different prediction models are contrasted in this study: Random Forest Classifier (RFC), Decision Tree Classifier (DTC), K-Neighbors Classifier (KNC), Support Vector Classifier (SVC), Gradient Boosting Classifier (GBC), Logistic regression (LR), Support vector Regressor (SVR), Random Forest Regressor(RFR), Gradient Boosting Regressor(GBR) and Decision Tree Regressor(DTR). Using these characteristics, the models are trained and evaluated. Evaluation criteria such as accuracy, kappa value, AUC-ROC, and confusion matrix are used to find the models' effectiveness. In order to determine which model performed the best, the assessment results are compared with one another. Based on study findings, the Gradient Boosting Classifier outperforms the other models tested by a significant margin (96 per cent). This model's AUC-ROC of 0.97 indicates it does a decent job at separating the positive and negative categories. 2023 IEEE.
- Source
- 2023 2nd International Conference for Innovation in Technology, INOCON 2023
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Accuracy Scores; Classification report; Classifier; Machine Learning; Regressors
- Coverage
- Basha M.S.A., Gandhi Institute of Technology and Management (Deemed to Be University), GITAM School of Business, Bengaluru, India; Prabhavathi C., Deemed to Be University, Department of Professional Studies Christ, Bengaluru, India; Khangembam V., Deemed to Be University, Department of Professional Studies Christ, Bengaluru, India; Sucharitha M.M., Deemed to Be University, Department of Professional Studies Christ, Bengaluru, India; Oveis P.M., Gandhi Institute of Technology and Management (Deemed to Be University), GITAM School of Business, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835032092-3
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Basha M.S.A.; Prabhavathi C.; Khangembam V.; Sucharitha M.M.; Oveis P.M., “Predicting Graduate Admissions using Ensemble Machine Learning Techniques: A Comparative Study of Classifiers and Regressors,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/19977.