• K Radha
• K Bhanu Prakash

: In the past years, IT industry has been going through a problem of high attrition which results in economic loss and members of staff may leave the Organizations. Main objective of this paper is to evolve a prototype to predict member of staff attenuation and to determine the organizational chances to solve any issues by improving job security. Member of Staff track record is collected from HR databases from the three different Organizations in India. In the Results, it was shown that the accuracy logistic regression algorithm is 85%. It is performing better when predicting the member of staff attrition that who are leaving the organization than who are not leaving the organization. Member of staff Attrition is considered as the Number of Situations that loses the members of staff in the Organization due to retirement and resignation, or dismissal in company’s interest. The objective of this paper is to find out if there any relation in between given dataset and attrition. If there exists any such relation, how they must take actions in order to make less member of staff attrition.

"Prediction of the Staff Exhaustion with Machine Learning Classifier", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.1830-1837, April-2020, Available :http://www.jetir.org/papers/JETIR2004450.pdf

"Prediction of the Staff Exhaustion with Machine Learning Classifier", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp1830-1837, April-2020, Available at : http://www.jetir.org/papers/JETIR2004450.pdf