基于Flask的员工离职数据可视分析系统设计与实现

Design and Implementation of Employee Turnover Data Visualization and Analysis System Based on Flask

摘要: 本论文介绍了一个基于可视化分析技术的员工离职数据可视分析平台的开发，旨在预测员工离职并为企业提供科学直观的管理手段。通过使用随机森林模型进行员工离职预测，并结合多种图表数据挖掘，系统能够帮助企业管理者了解员工离职的原因和规律，为人力资源管理提供决策支持。论文中详细介绍了随机森林模型的原理和优势，并验证了其在员工离职预测上的高准确率。可视化分析系统的功能需求包括员工离职时间规律观察、员工离职空间规律观察、预测模型可视化和交互体验优化等。系统通过图表展示了员工离职与各个变量之间的关联程度，包括时间规律、空间规律、员工满意度和绩效对离职的影响。此外，用户可以通过提交当期人力资源管理数据进行模型训练和预测，并通过邮件通知功能及时通知各部门领导有离职可能的员工。这个可视化分析平台为企业管理者提供了一个科学、直观且易用的数据分析工具，帮助他们更好地了解员工流动问题，提高企业的经济效益和运营稳定性。然而，系统仍存在一些局限性，未来的研究可以考虑引入其他更复杂的模型或算法来进一步提高预测准确性和泛化能力。

Abstract: This paper introduces the development of an employee turnover data visualization and analysis platform based on visual analysis technology, aimed at predicting employee turnover and providing scientific and intuitive management approaches for enterprises. By using the Random Forest model for employee turnover prediction and combining multiple chart data mining techniques, the system assists enterprise managers in understanding the reasons and patterns of employee turnover, thereby providing decision support for human resources management. The paper elaborates on the principles and advantages of the Random Forest model and verifies its high accuracy in employee turnover prediction. The functional requirements of the visualization and analysis system include observing the time and spatial patterns of employee turnover, visualizing the prediction model, and optimizing interactive experience. The system presents the correlations between employee turno-ver and various variables, including time patterns, spatial patterns, employee satisfaction, and performance impact on turnover, through graphical representation. Additionally, users can submit current human resources management data for model training and prediction, and timely notify department leaders of employees at risk of turnover through email notification. This visualization and analysis platform offers enterprise managers a scientific, intuitive, and user-friendly data analysis tool to better understand employee mobility issues and improve economic efficiency and oper-ational stability. However, the system still has some limitations, and future research could consider introducing more complex models or algorithms to further enhance prediction accuracy and generalization capability.