Data Mining Approach in Personnel Selection: The Case of the IT Department

Abstract

Data mining studies have been frequently included in the literature recently. Data mining can be applied in every field, especially in banking, marketing, customer relationship management, investment and portfolio management. In the literature, the problem of personnel selection has been examined with the help of multi-criteria decision-making techniques. In this study, it has been aimed to apply data mining techniques in the field of human resources where relatively little has been used. The features of a large-scale construction company have been determined according to the competencies specified in the information technologies department announcement. The candidates were ranked according to these attributes. While ranking, accuracy values have been compared by using basic algorithms of data mining techniques. While applying the process steps, the necessary data pre-processing techniques have been applied to candidates who entered incomplete or incorrect information during the application process. Basically, the decision trees algorithm gave the highest accuracy. Also, random forest, adaboost, gradient boosting, and xgboost algorithms have been tried. In addition, it has found the attributes that should be looked at first in the application features. The high number of data enabled machine learning to learn information more easily and to weigh the existing criteria easily. With this study, it has been aimed to obtain a more objective result by weighting with machine learning algorithms instead of weighting the personnel selection problem with multi-criteria decision-making methodology. In addition, it is an extremely difficult process to interview candidates for recruitment under the current Covid-19 pandemic conditions that the whole world and our country are struggling with. Online conversations take a lot of time. With this study, it has been aimed to provide optimization by automating the process by weighting the features related to the existing data in the process. The study has been done in the WEKA and Python program.