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1702543 Vol 4 · Issue 6 Download Paper

Leveraging the accuracy of the Employee Attrition model: A Machine Learning Approach

Tanmay Dhamdhere Dr. Vipul Dalal

Subject area: Science,Engineering and Technology  ·  Area of research: Machine Learning

Abstract

Employee attrition is a process in which the employees working in a company quits his/her job due to various reasons. For instances due to retirement, sacking from the organization or personal reasons. The task of churning the employee is an hectic one, as there are no fixed pattern or formula which can give an accurate prediction of whom to churn. But when implemented, It can certainly lead to an winning situation for the companies. The companies will be largely benefitted by the attrition of the employees as it can significantly reduce the cost of the labors, also it can bring an overall changes which can positively affect the company?s growth. Employees are the backbone for any company to bloom. There are certain factors such as age, increment, pay and many more which comes in picture for attrition of the employee. Using proper methodology and planning, it will be easy for a company to churn out the wrong employee and to proliferate their progress. In this paper, we have proposed a suitable method, which uses the techniques related to Machine Learning to yield an accuracy of 81.31%. Using this strategy, an overall accuracy of 96% can be achieved which can potentially help the companies towards its goal.

Keywords

Machine Learning, attrition, churn, goal

References

[1] N.Shilpa,A Study on Reasons of Attrition and Strategies for Employee Retention,IJERA,Dec 2015

[2] Saurabh Khanolkara,Mayuresh Gaitondea,Vishal Dabgotra, Leveraging the Efficiency of the Customer Retention Process: A Deep Learning Approach,IRJET,May 2020

[3] Tara Rawat,Dr.Vineeta Khemchandani, Feature Engineering (FE) Tools and Techniques for Better Classification Performance, International Journal of Innovations in Engineering and Technology (IJIET),May 2019

[4] Sebastian Raschka,Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning,arXiv,2018

[5] Khushbu Kumari, Suniti Yadav, Linear regression analysis study, Journal of the Practice of Cardiovascular Sciences, January 2018

[6] Xue Ying , An Overview of Overfitting and its Solutions, IOP Conf. Series: Journal of Physics,2019

[7] Jianyu Miao, Lingfeng Niub, A Survey on Feature Selection, Information Technology and Quantitative Management (ITQM 2016)

[8] Ratnadeep R. Deshmukh, Vaishali Wangikar, Data Cleaning: Current Approaches and Issues, IEEE International Conference on Knowledge Engineering,Jan 2011

[9] M. Mostafizur Rahman and Darryl N. Davis, Machine Learning Based Missing Value Imputation Method for Clinical Datasets,Springer,2013

[10] Dr. K.Sunanda, AN EMPIRICAL STUDY ON EMPLOYEE ATTRITION IN IT INDUSTRIES- WITH SPECIFIC REFERENCE TO WIPRO TECHNOLOGIES, ISSN (Online)2231-2528,Sep 2017

How to cite this paper

Tanmay Dhamdhere, Dr. Vipul Dalal "Leveraging the accuracy of the Employee Attrition model: A Machine Learning Approach " Iconic Research And Engineering Journals Volume 4 Issue 6 2020 Page 30-33
Tanmay Dhamdhere, Dr. Vipul Dalal "Leveraging the accuracy of the Employee Attrition model: A Machine Learning Approach " Iconic Research And Engineering Journals, vol. 4, no. 6, Dec. 2020
Tanmay Dhamdhere, Dr. Vipul Dalal (2020). Leveraging the accuracy of the Employee Attrition model: A Machine Learning Approach . Iconic Research And Engineering Journals, 4(6).
Tanmay Dhamdhere, Dr. Vipul Dalal "Leveraging the accuracy of the Employee Attrition model: A Machine Learning Approach " Iconic Research And Engineering Journals, vol. 4, no. 6, Dec. 2020.
@article{1702543,
      author = {Tanmay Dhamdhere, Dr. Vipul Dalal},
      title = {Leveraging the accuracy of the Employee Attrition model: A Machine Learning Approach },
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {4},
      number = {6},
      pages = {30-33},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/17025436.pdf},
      abstract = {Employee attrition is a process in which the employees working in a company quits his/her job due to various reasons. For instances due to retirement, sacking from the organization or personal reasons. The task of churning the employee is an hectic one, as there are no fixed pattern or formula which can give an accurate prediction of whom to churn. But when implemented, It can certainly lead to an winning situation for the companies. The companies will be largely benefitted by the attrition of the employees as it can significantly reduce the cost of the labors, also it can bring an overall changes which can positively affect the company?s growth. Employees are the backbone for any company to bloom. There are certain factors such as age, increment, pay and many more which comes in picture for attrition of the employee. Using proper methodology and planning, it will be easy for a company to churn out the wrong employee and to proliferate their progress. In this paper, we have proposed a suitable method, which uses the techniques related to Machine Learning to yield an accuracy of 81.31%. Using this strategy, an overall accuracy of 96% can be achieved which can potentially help the companies towards its goal.},
      keywords = {Machine Learning, attrition, churn, goal},
      month = {December},
  }