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首页> 外文会议>IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology >Comparison of Machine Learning Techniques to Predict the Attrition Rate of the Employees
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Comparison of Machine Learning Techniques to Predict the Attrition Rate of the Employees

机译：机器学习技术的比较预测员工的磨损率

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摘要

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，员工的磨损是当今世界中最伟大的担忧之一。这背后的原因可能是由于个人或公司相关的问题，如长途旅行，没有工作生活平衡，薪酬较少，没有工作满意度等。根据商人的一项研究，员工的磨损结果从他们的帖子中辞职，退休，疾病或消亡。考虑到这些问题，该项目旨在使用预处理技术（EDA），特征选择技术等预处理技术，利用诸如逻辑回归等各种机器学习技术，找到最有可能从组织中获得的员工。机器（SVM）和随机森林。根据哪些课程可以由组织合并，以最大限度地减少磨损率，也有助于建设和维持员工和组织之间的强大关系。

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