Machine Learning using Recursive Feature Elimination of Students’ Data in Singapore During Pre-Covid and Covid

Student career management is a critical activity in the education sector. Machine learning method, Recursive Feature Elimination (RFE), has been used to find key features for prediction tasks with the help of Students’ data. Using RFE, which is a machine learning technique, massive datasets are analyzed, and the most pertinent features are chosen for predictions, increasing computing speed and accuracy. RFE has been combined with several machine learning techniques for feature selection, such as support vector machines (SVM), decision trees, and random forests, and many more. This study focuses on comprehending students’ data and evaluates the effect on graduate employability based on predictive power of Machine Learning algorithms. Post COVID, the data imply that the COVID-19 epidemic has significantly hampered the graduate employment for governments, corporations, and the people. The study offers potential answers in analyzing the independent variables that affect the employability aspects using Machine Learning Algorithm's performances in dealing with pre-COVID and COVID datasets.