**Author details**

Within the scope of this study, the leadership qualities that employees qualify for their leaders are classified. According to the analysis results obtained, the most successful data mining algorithm in terms of classification has been random forest algorithm with a rate of 81.3725%. This algorithm is a community learning algorithm. This algorithm generates more than one decision tree during the classification process and thus aims to increase the classification value. Individually constructed decision trees come together to form the decision forest. The

In this study, some results were obtained by applying "Classification" methods of data mining methods. With the use of classification algorithms in leadership, an analysis of the effect of leaders' leadership styles and behaviors on the motivation of site employees has been made

According to the MLQ licensed questionnaire score, the leadership style tendencies of construction site supervisors were categorized and evaluated in three different ways as "transformational," "transactional" and "passive/avoidant" within the scope of responses obtained

The following suggestions can be made to the construction sector, construction companies and construction site managers who are the managers of the construction sites in order to

Among the important results of the study, it can be said that sector representatives will benefit from the fact that the motivation of employees can be increased if each employee group has different qualifications and if these leaders are selected and implemented in accordance

At the same time, based on the information obtained as a result of this study, it is considered that important shortcomings can be achieved by sharing the results of the construction sector, which can raise the motivation of the construction site employees with the representatives of the construction sector. In this context, it is thought that it would be beneficial to share the results obtained with the site chiefs and employees in the construction companies with the

This study was supported by Adana Science and Technology University Scientific Research Project Commission (Project Number: 17103018 and Project Number: MÜHDBF.BM.2015-

overcome the deficiencies found in the subject in the data presented in this study.

classification results obtained are given in **Table 3**.

from the survey questionnaires in this study.

meetings, seminars and similar sessions.

**5. Conclusion**

44 Data Mining

in the construction industry.

with these qualities.

**Acknowledgements**

2111), Turkey.

Abdullah Emre Keleş<sup>1</sup> and Mümine Kaya Keleş<sup>2</sup> \*

\*Address all correspondence to: mkaya@adanabtu.edu.tr

1 Faculty of Engineering, Department of Civil Engineering, Adana Science and Technology University, Adana, Turkey

2 Faculty of Engineering, Department of Computer Engineering, Adana Science and Technology University, Adana, Turkey
