International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Since Year 2012 | Open Access | Double Blind Reviewed

ISSN: 2319-7064




Downloads: 84

Research Paper | Software Engineering | India | Volume 5 Issue 8, August 2016


Attrition Prediction Using Machine Learning to Help in Astute Decision

Reshad Abdullah | Sachin Bojewar


Abstract: Industries, especially IT (Information Technology) today, are experiencing high employee attrition rate. The employee leaving voluntarily is not good for organization or to project in which they are working. Hence HR and senior managers and the policy makers of any industry are working together to reduce this voluntary exit. A good leader senses and understands employee needs and work with them and HR to fix the issues. However not all attrition causes are known to managers and when it actually happens it turns out as a surprise, then they are not able to do much. Some amount of attrition is certain and bound to happen like employee retiring or death of employee hence the scope of this work is only restricted to voluntary exit [1]. Organization and HR department has felt that if they would have known earlier, or they could have picked the sign of exit, they might have prevented good employees leaving. With vast amount of historical data available within the organization, and through analytics & machine learning it is possible to predict attrition. These tools not only predict but also show some clear pattern in attrition. Many organizations today uses cots attrition prediction tool or build their own in-house prediction tool. The scope of this work is implementation of my theory paper published Attrition Prediction- Need of the Hour for Companies [1]. A tool is developed to predict attrition and it also predicts reason for attrition, this tool is based on decision tree algorithm and developed in R language. The factor or reasons for attrition are then effectively used by managers and HR department to design a retention strategy for the employee or proactively find his replacement. At the same time management becomes aware of the situation and are in position to predict how much new backup recruitment can be done in future.


Keywords: Attrition, COTS, C45, C50, ID3, exp, Model, Machine learning, Notice period, Retention, r_dt10


Edition: Volume 5 Issue 8, August 2016,


Pages: 1366 - 1370


How to Cite this Article?

Reshad Abdullah, Sachin Bojewar, "Attrition Prediction Using Machine Learning to Help in Astute Decision", International Journal of Science and Research (IJSR), Volume 5 Issue 8, August 2016, pp. 1366-1370, https://www.ijsr.net/get_abstract.php?paper_id=ART20161174

How to Share this Article?

Enter Your Email Address




Similar Articles with Keyword 'exp'

Downloads: 74

Research Paper, Software Engineering, India, Volume 4 Issue 8, August 2015

Pages: 570 - 573

Role of Automated GUI Testing in Secure Software Development of Indian Spacecraft Ground Software, GEOSCHEMACS

K. V. Maruthi Prasad | J. Krishna Kishore

Share this Article

Downloads: 89

Review Papers, Software Engineering, India, Volume 4 Issue 6, June 2015

Pages: 1991 - 1994

Methodologies and Technique for Software Agent

Kiran [228] | Vijay [457]

Share this Article


Top