Predicting the percentage of student placement: A comparative study of machine learning algorithms

Abstract

In recent years, there has been an increase in the demand for higher education in Turkey, where the demand, as in most other countries, exceeds what is available. The main purpose of this research is to develop machine learning algorithms for predicting the percentage of student placement based on the data related to the university's academic reputation, opportunities of the city where the university is located, facilities and cultural opportunities of the university. When the model accuracy was evaluated on the basis of performance metrics, the Extreme Gradient Boosting (XGBoost) algorithm showed greater predictive accuracy than other machine learning approaches. A sensitivity analysis was performed using the extreme gradient boosting machines algorithm to identify the degree to which the input variables contribute to the determination of the output variable. Five input variables, namely the percentage of student placement at year t-1, the university scientific document score, university Phd programme score, university faculty member/student score, and the percentage of student placement at year t-2 were found to be the most effective parameters. Prediction and sensitivity analysis results obtained in this study can be used in many different ways, such as determining the quotas for universities, allocating resources, and making new regulations.

Comparative analysis of machine learning algorithms for predicting standard time in a manufacturing environment

“…The XGBoost algorithm works like this: consider a dataset with m features and an n number of instances . By reducing the loss and regularization goal, we should ascertain which set of functions works best.where l represents the loss function, f k represents the ( k -th tree), to solve the above equation, while Ω is a measure of the model's complexity, this prevents over-fitting of the model (Çakıt and Dağdeviren, 2022).…”

“…where l represents the loss function, f k represents the (k-th tree), to solve the above equation, while Ω is a measure of the model's complexity, this prevents over-fitting of the model (Çakıt and Dağdeviren, 2022).…”

Career Path Insights

“…Besides, Beaulac and Rosenthal [12] (2019) and Çakıt and Dağdeviren [13] (2022) dig into foreseeing scholastic achievement and majors utilizing AI, offering a more extensive viewpoint past forecast of understudy situation. Different examinations, for example, those by Aravind et al [14] All in all, the writing gives sign of developing interest in anticipating understudy position and scholastic achievement utilizing AI models as well as related instructive results.…”

Göz izleme verilerine bağlı olarak zihinsel iş yükünü sınıflandırmada makine öğrenmesi algoritmalarının kullanılması

“…2.2.7. Aşırı gradyan artırma (XGBoost) Aşırı Gradyan Artırma (XGBoost), gradyanı artırılmış (gradient boosted) karar ağaçları temeline dayalı çalışan ölçeklenebilir bir makine öğrenme algoritmasıdır [41,42]…”