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Comparison of Machine Learning Techniques to Predict the Attrition Rate of the Employees

机译:机器学习技术预测员工流失率的比较

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In most of the organizations, Employee Attrition has been one of the greatest concerns in today's world. The reason behind this can be due to personal or company related issues such as long- distance travelling, no work life balance, less salary hike, no job satisfaction etc. According to a study done by Businessdictonary, employee attrition results from resigning from their post, retirement, illness, or demise. Considering these issues, the project aims to find the employees who are most likely to attrite from the organization using pre-processing techniques such as exploratory data Analysis (EDA), feature selection techniques and utilizing various machine learning techniques such as Logistic Regression, Support Vector Machine (SVM) and Random Forest. According to which several programs can be incorporated by the organizations to minimize the attrition rate and also help in building and maintaining a robust relationship between the employees and the organization.
机译:在大多数组织中,员工流失一直是当今世界最关注的问题之一。其背后的原因可能是由于与个人或公司相关的问题,例如长途旅行,没有工作生活平衡,加薪少,工作满意度降低等。根据Businessdictonary所做的一项研究,员工辞职是员工辞职的结果,退休,患病或死亡。考虑到这些问题,该项目旨在使用诸如探索性数据分析(EDA),特征选择技术之类的预处理技术以及利用诸如Logistic回归,支持向量的各种机器学习技术来寻找最有可能在组织中受聘的员工。机器(SVM)和随机森林。组织可以据此合并几个程序,以最大程度地减少人员流失率,并帮助建立和维护员工与组织之间的牢固关系。

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