PREDICTION OF EMPLOYEE ATTRITION USING DATAMINING

J. Rajanikanth · V. V. Sivaramaraju · K. V. S. S. R. Murthy

56 citations0/0 files storedIEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

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AI and HR Technologies Online Learning and Analytics Customer churn and segmentation

Abstract

Now a day’s Employee Attrition prediction become a major problem in the organizations. Employee Attrition is a big issue for the organizations specially when trained, technical and key employees leave for a better opportunity from the organization. This results in financial loss to replace a trained employee. Therefore, we use the current and past employee data to analyze the common reasons for employee attrition or attrition. For the prevention of employee attrition, we applied a well known classification methods, that is, Decision tree, Logistic Regression, SVM, KNN, Random Forest, Naive bayes methods on the human resource data. For this we implement feature selection method on the data and analysis the results to prevent employee attrition. This is helpful to companies to predict employee attrition, and also helpful to their economic growth by reducing their human resource cost.

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References

2008Handling class imbalance in customer churn prediction 2011New insights into churn prediction in the telecommunication sector: A profit driven data mining approach 2002Turning telecommunications call details to churn prediction: a data mining approach 2006Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques 2011Customer churn prediction in telecommunications 2010Employee churn prediction 2008Integrating the voice of customers through call center emails into a decision support system for churn prediction 2009Improved marketing decision making in a customer churn prediction context using generalized additive models

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