PREDICTION OF EMPLOYEE ATTRITION USING DATAMINING

Shiva Shankar Reddy, J. Rajanikanth, V. Sivaramaraju, K. VSSR Murthy
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引用次数: 23

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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利用数据挖掘预测员工流失
如今,员工流失率预测已成为企业面临的一个重大问题。员工流失对组织来说是一个大问题,特别是当受过培训的技术和关键员工离开组织寻找更好的机会时。这导致了更换训练有素的员工的经济损失。因此,我们使用当前和过去的员工数据来分析员工流失或离职的常见原因。为了防止员工流失,我们对人力资源数据应用了众所周知的分类方法,即决策树、逻辑回归、支持向量机、KNN、随机森林、朴素贝叶斯等方法。为此,我们对数据实施特征选择方法,并对结果进行分析,以防止员工流失。这有助于企业预测员工流失,也有助于企业通过降低人力资源成本来实现经济增长。
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