An approach for predicting employee churn by using data mining

Name: An approach for predicting employee churn by using data mining
Author: Yiğit, İ. O., Shourabizadeh, Hamed

İsim	An approach for predicting employee churn by using data mining
Yazar	Yiğit, İ. O., Shourabizadeh, Hamed
Basım Tarihi:	2017
Basım Yeri	- IEEE
Konu	Employee churn prediction, Data analysis, Feature selection, Data mining, Classification
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-5386-1880-6
Kayıt Numarası	97654550-765a-4247-8dc7-496509c59f2f
Tarih	2017
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	Employee churn prediction which is closely related to customer churn prediction is a major issue of the companies. Despite the importance of the issue, there is few attention in the literature about. In this study, we applied well-known classification methods including, Decision Tree, Logistic Regression, SVM, KNN, Random Forest, and Naive Bayes methods on the HR data. Then, we analyze the results by calculating the accuracy, precision, recall, and F-measure values of the results. Moreover, we implement a feature selection method on the data and analyze the results with previous ones. The results will lead companies to predict their employees' churn status and consequently help them to reduce their human resource costs.
DOI	10.1109/IDAP.2017.8090324

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An approach for predicting employee churn by using data mining

Yazar Yiğit, İ. O., Shourabizadeh, Hamed

Basım Tarihi 2017

Basım Yeri - IEEE

Konu Employee churn prediction, Data analysis, Feature selection, Data mining, Classification

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-5386-1880-6

Kayıt Numarası 97654550-765a-4247-8dc7-496509c59f2f

Tarih 2017

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin Employee churn prediction which is closely related to customer churn prediction is a major issue of the companies. Despite the importance of the issue, there is few attention in the literature about. In this study, we applied well-known classification methods including, Decision Tree, Logistic Regression, SVM, KNN, Random Forest, and Naive Bayes methods on the HR data. Then, we analyze the results by calculating the accuracy, precision, recall, and F-measure values of the results. Moreover, we implement a feature selection method on the data and analyze the results with previous ones. The results will lead companies to predict their employees' churn status and consequently help them to reduce their human resource costs.

DOI 10.1109/IDAP.2017.8090324