A Study on Predicting Skilled Employees’ Using Machine Learning Techniques

Abstract

Management of human resource (HR) has become one of the key considerations of entrepreneurs and CEOs in nearly every industry, with the objective of encouraging practices for the proper disclosure of exceptionally qualified employees. Appropriately, the board is associated with these employees’ results. Especially to guarantee that the ideal individual is distributed with perfect timing to the convenient work. From here, data mining is getting popular as a viable option that its point is to find data from enormous measures of data. A model was developed as part of this study using data collection techniques to estimate the results of employees by using the dataset prepared by the Civil Aviation Ministry of Egypt, which had prepared a survey for 145 works and circulated it. In this, three key data mining methods have been utilized to foster the model of classification and to characterize the main factors that positively affect execution. The techniques are support vector machine (SVM), Naïve Bayes, and the decision tree (DT). To acquire an exceptionally exact model, a few experiments were completed in view of the previous strategies utilized in the WEKA tool to permit decision making, HR experts to expect and work on their employees’ productivity.