Prediction of Employee Attrition

Employee attrition is basically downfall in an organization where employees depart. They are the most important bodies of an organization who contributes in the growth of organization. In an organization, they are regarded as its core. Employee performance is a key factor in determining an organization’s overall performance. Therefore, it’s necessary to find out whether or not employees are content and whether they have any additional motivations for quitting.Based on research, different supervised machine learning algo- rithms such as Logistic Regression, Decision Tree, Naive Bayes Classifier, Random Forest Classifier, and SVM are compared. Comparatively, Random Forest algorithm gives better accuracy on employee attrition dataset. As a result, Random Forest Clas- sification method is used in the research for predicting employee attrition. Here the Machine Learning algorithm uses the Kaggle employee attrition dataset. It includes 35 attributes for 1470 employees such as employee performance, average monthly hours at work, Salary, Job satisfaction, Years in company and others as features. As observed, Random Forest makes predictions with approximately 87.46% accuracy.