Engineering College Admission Predictor Using Bagging Ensemble Modelling

Currently, a number of students make mistakes when making their college choice list for a number of reasons, such as inaccurate college analysis, insufficient knowledge, and nervous projection. The efficiency of admissions processes has a major impact on how well higher education institutions perform. In this study, we offer a data mining method for forecasting engineering program acceptance of students. Our focus is on obtaining critical information from the student’s academic record, including test results, grades, and other relevant data. In order to predict each student’s acceptance chances, we next feed these data into a machine learning model, such as a Random Forest or decision tree. By using the power of data mining, we can predict the engineering admissions result more accurately and effectivel than we could with earlier methods. We also compare the performance of our model with other, more traditional methods like logistic regression. According to our conclusion, our method performs better at predicting admission than conventional methods. In addition, we examine the implications of our results and suggest possible enhancements to the accuracy of our model.