Churn Prediction of Employees Using Machine Learning Techniques

Employees are considered as the most valuable assets of any organization. Various policies have been introduced by HR professionals to create a good working environment for them, but still, rate employees quitting Technology Industry is quite high. Often reason behind their early attrition could be due company-related or personal issues, such No satisfaction at workplace, Fewer opportunities learning, Undue Workload, Less Encouragement, and many others. This paper aims in discussing structured way predicting churn implementing various Classification techniques like SVM, Random Forest classifier, Naives Bayes classifier. The performance classifiers was compared using metrics Confusion Matrix, Recall, False Positive Rate, Accuracy determine best model prediction. We found that among models, classifier proved IT employee A Correlation Matrix generated form heatmap identify important features might impact rate.