Makine öğrenmesi algoritmaları kullanarak öğrencilerin akademik başarısını etkileyen faktörlerin tespit edilmesi

The aim of this study was to identify the factors affecting students' academic achievement usingmachine learning algorithms. The first data covered in the study, the data obtained from a study conductedin the Assam state of India were used. The second data covers, data collected from two public schools in2005 from the Alentejo region of Portugal were used. The application framework in the study wasdeveloped under the cross industry standard process for data mining. Using classification models withRandom Forest, Extreme Gradient Boosting and Support Vector Machines, important factors affectingstudents' academic success were examined. The results obtained were verified and compared utilizingclassification Accuracy, F-1 Score results. By creating classification models with Random Forest, ExtremeGradient Reinforcement and Support Vector Machines, important factors affecting students' academicsuccess were examined. According to these results, it can be said that Extreme Gradient Boosting providesthe best results in identifying the factors affecting students' academic achievement. Besides these results, itwas predicted success scores and the identified factors were compared with similar studies in the literatureand it was seen that they showed significant similarity