Using Machine Learning Algorithms to Predict Turnover - An Application in the Iron and Steel Industry

Abstract

Since the cost of retaining the existing staff is lower than the cost of hiring a new staff for many companies, including companies in the Iron and Steel Sector, which have challenging working conditions, it aims to have a low labor turnover rate. In this study, machine learning algorithms are used to predict the personnel who are likely to leave the job. The machine learning algorithms in the study were implemented through Knime and Python programming languages. In the application, the data, which took place in an Iron and Steel company in a 16-year period, consists of 14 different attributes that may be effective on the resignation of 2318 personnel. Six different machine learning algorithms were applied and the most successful of them was the Random Forest Algorithm with an accuracy rate of approximately 78%. Therefore, the most important criteria leading to turnover were determined using the Random Forest Algorithm. As a result of this study, it will be possible to make decisions such as whether the existing staff has the potential to leave the job, which criteria should be prioritized in recruitment and the necessary actions to be taken to ensure the continuity of the staff.

Keywords

• Machine Learning
• Random Forest Algorithm
• Turnover Rate
• Turnover Prediction

İşten Ayrılma Durumunun Tahminlenmesinde Makine Öğrenmesi Algoritmaları Kullanımı – Demir Çelik Sektöründe Bir Uygulama

Öz

Zorlu çalışma koşullarına sahip demir-çelik sektöründeki birçok firma, mevcut personeli elde tutmanın maliyetinin yeni bir çalışan işe almaktan daha düşük olması nedeniyle çalışan devir oranını düşürmeye odaklanmaktadır. Bu çalışmanın amacı, makine öğrenmesi algoritmaları kullanarak işten ayrılma riski olan çalışanları tahmin etmektir. Çalışma kapsamında, Knime ve Python programları kullanılarak bir demir-çelik firmasının 16 yıllık sürede toplam 2318 çalışan için 14 farklı niteliği içeren veriler analiz edilmiştir. Çalışmada beş farklı makine öğrenmesi algoritması uygulanmış ve en yüksek doğruluk oranına (%78,02) Rassal Orman Algoritması ile ulaşılmıştır. Rassal Orman Algoritması yardımıyla, işten ayrılmalara yol açabilecek en önemli faktörler belirlenmiştir. Bu çalışmanın sonuçları; mevcut personelin işten ayrılma olasılığını değerlendirme, işe alımlarda hangi kriterlere öncelik verilmesi gerektiğini belirleme ve çalışan bağlılığını artırmaya yönelik stratejik kararlar geliştirme süreçlerinde işletmelere rehberlik edecektir.

Anahtar Kelimeler

• Makine Öğrenmesi
• Rassal Orman Algoritması
• Devir Oranı
• İşten Ayrılma Tahminlenmesi

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