Comparison of Machine Learning Techniques to Predict the Attrition Rate of the Employees

In most of the organizations, Employee Attrition has been one of the greatest concerns in today's world. The reason behind this can be due to personal or company related issues such as long- distance travelling, no work life balance, less salary hike, no job satisfaction etc. According to a study done by Businessdictonary, employee attrition results from resigning from their post, retirement, illness, or demise. Considering these issues, the project aims to find the employees who are most likely to attrite from the organization using pre-processing techniques such as exploratory data Analysis (EDA), feature selection techniques and utilizing various machine learning techniques such as Logistic Regression, Support Vector Machine (SVM) and Random Forest. According to which several programs can be incorporated by the organizations to minimize the attrition rate and also help in building and maintaining a robust relationship between the employees and the organization.

[1]  Girish Keshav Palshikar,et al.  Employee churn prediction , 2011, Expert Syst. Appl..

[2]  nbspMitushi Modi,et al.  An evaluation of filter and wrapper methods for feature selection in classification , 2014 .

[3]  Factors influencing employee attrition in Indian ITeS call centres , 2011 .

[4]  Qasem A. Al-Radaideh,et al.  Using Data Mining Techniques to Build a Classification Model for Predicting Employees Performance , 2012 .

[5]  Bart Baesens,et al.  New insights into churn prediction in the telecommunication sector: A profit driven data mining approach , 2012, Eur. J. Oper. Res..

[6]  M. Tahar Kechadi,et al.  Customer churn prediction in telecommunications , 2012, Expert Syst. Appl..

[7]  Phayung Meesad,et al.  A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition , 2014, Expert Syst. Appl..

[8]  Tom. Mitchell GENERATIVE AND DISCRIMINATIVE CLASSIFIERS: NAIVE BAYES AND LOGISTIC REGRESSION Machine Learning , 2005 .

[9]  Xiaohui Lin,et al.  A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information. , 2012, Journal of chromatography. B, Analytical technologies in the biomedical and life sciences.

[10]  Gwendolyn M. Combs,et al.  Managing BPO Service Workers in India: Examining Hope on Performance Outcomes , 2010 .

[11]  R. Vijesh,et al.  Employee attrition - a pragmatic study with reference to BPO industry , 2012, IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012).