Yapay öğrenme teknikleriyle milli eğitim bakanlığı’nda yarı zamanlı öğretmenlerin görevlendirilmesi

In this study, the data for part-time teachers who will work in schools were analyzed using various classification algorithms to observe the success measurement of the assignments and automate the process with artificial intelligence-supported classification algorithms. In the study, the performance of the data obtained through the applications made through a web-based application, for the assignment criteria made in line with the needs, was measured. For this, the attributes for the appointment of 894 candidates in line with the needs from the system applied by 3053 candidates were studied. In the study conducted on six different classifiers, the Random Forest Classifier gave the best result with an accuracy value of 0.71 and an f-score of 0.77. With this study, it has been shown that this study can be automated with appropriate classifiers and used in this field according to the weights of the assignment criteria.