• Parv N Gandhi
• Ganesh S Mhaske
• Aishwarya Jangale
• Abhilasha Kadlag

Every organization has its own productivity and strength which stands of the legs of the employees. Keeping regular employee is a great challenge for all organization in the competitive world. Employee Attrition is one of the biggest business problems in HR Analytics. Companies invest a lot in the training of the employees keeping in mind the returns they would provide to the company in the future. If an employee leaves the company, it is the loss of opportunity cost to the company. These study interpreters the employee’s attrition rate through the related attributes like Job Role, overtime, job level affect the attrition largely. The paper contain the survey of various classification algorithms like logistic regression, LDA, SVM, KNN, Random Forests to predict the probability of attrition of any new employee.As a result, training balanced dataset with Random Forest achieved the second highest performance, with 0.269 F1-score but has achieved the highest accurancy with the algoritms we have used.

Employee attrition; Support vector machine; random forest; K nearest neighbours; Feature selection;Attrition Rate; HR; Classifier; Preprocessing; Employment Features

"Employee Attrition Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 9, page no.1086-1094, September-2020, Available :http://www.jetir.org/papers/JETIR2009148.pdf

"Employee Attrition Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 9, page no. pp1086-1094, September-2020, Available at : http://www.jetir.org/papers/JETIR2009148.pdf