Predicting Academic Performance of Immigrant Students Using XGBoost Regressor

The education sector has been effectively dealing with the prediction of academic performance of immigrant students, and the research associated with this domain proves beneficial enough for those countries where the ministry of education has to cater to such immigrants for altering and updating policies in order to elevate the overall education pedagogy for them. The present research begins with analyzing varied educational data mining and machine learning techniques that help in assessing the data fetched form PISA. It's elucidated that XGBoost stands out to be the ideal machine learning technique for achieving the desired results. Subsequently, the parameters have been optimized using the hyper parameter tuning techniques and implemented on the XGBoost Regressor algorithm. There is low error rate and higher level of predictive ability using the machine learning algorithms, which assure better predictions using the PISA data. The final results have been discussed along with future research work.