Çalışan Yıpranmasının ve Yıpranmaya Neden Olan Faktörlerin Tahmininde Makine Öğrenimi Yaklaşımı

Year 2021, Volume: 36 Issue: 4, 913 - 928, 29.12.2021

İrem Ersöz Kaya Oya Korkmaz

https://doi.org/10.21605/cukurovaumfd.1040487

Cited By: 4

Abstract

İşletmeler için oldukça önemli olan insan kaynağının yıpranmasının ve yıpranmanın doğal sonucu olan işten ayrılmanın önüne geçmek amacıyla yapılan bu çalışmada, yıpranmaya neden olan faktörler tahmine dayalı analitik tekniklerinden biri olan makine öğrenmesi yöntemleri kullanılarak belirlenmeye çalışılmıştır. Analiz için örnek veri seti IBM şirketi Watson Analytics programı kapsamında sunulan bir veri tabanından alınmıştır. Veri seti, 1470 adet çalışanın 30 farklı özniteliğini içermektedir. Çalışmada, tahmin başarısını değerlendirmek amacıyla yedi farklı makine öğrenmesi algoritması kullanılmıştır. Yıpranmaya neden olan faktörlerin tespitinde ise kazanç oranı yaklaşımı tercih edilmiştir. Çalışmanın kilit noktası, bootstrap tekniği ile yeniden örnekleme yapılarak sınıfların örnek sayılarının dengelenmesidir. Sonuç olarak, yeniden örnekleme ile makine öğrenmesi yöntemlerinin anlamlı sonuçlar vermesi sağlanmış ve tahmin doğruluk performansı, kör test yapılmasına rağmen %80’ler seviyesine ulaşmıştır. Kazanç oranı ile yapılan öncelik sıralamasında ilk 20’de yer alan özelliğin, yıpranmaya neden olan öncelikli faktörler olabileceği belirlenmiştir.

Keywords

Çalışan yıpranması, Tahmin analitikleri, Makine öğrenmesi, Öznitelik seçimi, Veri madenciliği

Machine Learning Approach for Predicting Employee Attrition and Factors Leading to Attrition

Abstract

In this study that aims to prevent the attrition of human resource which is so important for enterprises, as well as to prevent the leave of employment which is the natural result of such attrition, employee attrition and factors causing attrition are tried to be determined by predictive analytics approaches. The sample dataset which contains 30 different attributes of 1470 employees was obtained for the analysis from a database provided by IBM Watson Analytics. In the study, seven different machine learning algorithms were used to evaluate the prediction achievements. The gain ratio approach was preferred in determining the factors causing attrition. The key point of the study was to cope with the imbalanced data through resampling with bootstrapping. Thereby, even in the blind test, prospering prediction performances reaching up to 80% accuracy were achieved in robust specificity without sacrificing sensitivity. Therewithal, the effective factors causing attrition were investigated in the study and it was concluded that the first 20 attributes ranked according to their gain ratio were sufficient in explaining attrition.

Keywords

Employee attrition, Predictive analytics, Machine learning, Feature selection, Data mining

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