https://iaeme.com/Home/journal/IJARET 3329 editor@iaeme.com International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 11, Issue 12, December 2020, pp. 3329-3341, Article ID: IJARET_11_12_313 Available online at https://iaeme.com/Home/issue/IJARET?Volume=11&Issue=12 Journal Impact Factor (2020): 10.9475 (Calculated by GISI) www.jifactor.com ISSN Print: 0976-6480 and ISSN Online: 0976-6499 DOI: https://doi.org/10.34218/IJARET.11.12.2020.313 © IAEME Publication Scopus Indexed EMPLOYEE ATTRITION PREDICTION IN INDUSTRY USING MACHINE LEARNING TECHNIQUES Dr. M. Subhashini 1 and Dr. R. Gopinath 2 1 Assistant Professor in Department of Computer Science, SrimadAndavan Arts and Science College (Autonomous) (Affiliated to Bharathidasan University), Tiruchirappalli, Tamil Nadu, India 2 D.Litt. (Business Administration) - Researcher, Madurai Kamaraj University, Madurai, Tamil Nadu, India ABSTRACT Companies are always looking for ways to keep their professional personnel on board in order to save money on hiring and training. Predicting whether or not a specific employee would depart will assist the organisation in making proactive decisions. Human resource problems, unlike physical systems, cannot be defined by a scientific-analytical formula. As a result, machine learning approaches are the most effective instruments for achieving this goal. In this study, a feature selection strategy based on a Machine Learning Classifier is proposed to improve classification accuracy, precision, and True Positive Rate while lowering error rates such as False Positive Rate and Miss Rate. Different feature selection techniques, such as Information Gain, Gain Ratio, Chi-Square, Correlation-based, and Fisher Exact test, are analysed with six Machine Learning classifiers, such as Artificial Neural Network, Support Vector Machine, Gradient Boosting Tree, Bagging, Random Forest, and Decision Tree, for the proposed approach. In this study, combining Chi-Square feature selection with a Gradient Boosting Tree classifier improves employee attrition classification accuracy while lowering error rates. Key words: Feature Selection, Employee Attrition, Classification, Error Rates, Accuracy. Cite this Article: M. Subhashini and R. Gopinath, Employee Attrition Prediction in Industry Using Machine Learning Techniques, International Journal of Advanced Research in Engineering and Technology, 11(12), 2020, pp. 3329-3341. https://iaeme.com/Home/issue/IJARET?Volume=11&Issue=12 1. INTRODUCTION Employee turnover is another name for employee attrition. Wearing down is a common problem, and it's more prevalent in today's industry. In the vast majority of associations, it is