Determination and Classification of Crew Productivity with Data Mining Methods

Abstract

Turkey is a developing country and the main axis of development is “construction.” The construction sector is in a position to create demand for goods and services produced by more than 200 subsectors, and this widespread impact is the most basic indicator of the sector’s “locomotive of the economy.” In the development of the construction industry, crew productivity plays a very important role. While businesses that do not measure their employees’ needs, their locations, and so on are suffering from various losses, rare businesses that take these parameters into account can profit. The identification of lead - ership types that will motivate employees has great importance in terms of construction businesses where the human element is the foreground. For this purpose, in the province of Adana, the relationship of productivity between the engineers working in construction companies and workers who work at lower departments of these engineers was exam- ined. In this study, bidirectional multiple leadership questionnaire (MLQ) was applied to construction site managers and employees, and according to this survey data, leadership and motivations/productivities were classified using data mining methods. According to the classification analysis results, the most successful data mining algorithm was random forest algorithm with a rate of 81.3725%.