Employability of Machine Learning in the Efficacious Model Performance of Human Resource Prediction Algorithm

Savar Sharma

Vol. 8, Issue 1, Jan-Dec 2022

Page Number: 76 - 87

Abstract:

A good ecological environment is crucial to attracting talents, cultivating talents, retaining talents and making talents fully effective. This study provides a solution to the current mainstream problem of how to deal with excellent employee turnover in advance, to promote the sustainable and harmonious human resources ecological environment of enterprises with a shortage of talent. This study obtains open data sets and conducts data pre-processing, model construction model optimisation, and describes a set of enterprise employee turnover prediction models based on RapidMiner workflow. The data pre-processing is completed with the help of the data statistical analysis software IBM SPSS Statistic and RapidMiner. Statistical charts, scatter plots and boxplots for analysis are generated to realise data visualisation analysis. Machine learning, model application, performance vector, and cross-validation through RapidMiner's multiple operators and workflows. Model design algorithms include support vector machines, naive Bayes, decision trees, and neural networks. Comparing the performance parameters of the algorithm model from the four aspects of accuracy, precision, recall and F1-score. It is concluded that the performance of the decision tree algorithm model is the highest. The performance evaluation results confirm the effectiveness of this model in sustainable exploring enterprise employee turnover prediction in human resource management.

References

• Catia oliveira, Tiago Guimares, Filipe Portela, Manuel Santos, “Benchmarking Business Analytics Techniques in Big Data,” Procedia Computer Science, vol.160, 2019,pp.690-695.
• Hom P W, Lee T W, Shaw J D, Hausknecht J P, “One Hundred Years of Employee Turnover Theory and Research,” Journal of Applied psychology, vol.102, 2017, pp.530－545.
• Wang Zhongjun, Wen Lin, Long Lirong, “Boundaryless Career: Review and Prospect of Two Decades of Research,” Journal of Psychological Science, vol.1, 2015, pp.243-248.
• Provost F., Fawcett T, “Business Problems and Data Science Solutions In Data Science for Business; Mike Loukides and Meghan Blanchette,” O'Reilly Media: Sebastopol, California, USA, 2013, pp.35–42.
• Ali Cloud Tianchi data set. HR Analytics, 5 March 2021. [Online]. Available: https://tianchi.aliyun.com/dataset/dataDetail?dataI d=92838.
• Li Jinchang, “Applied Sampling Technology. Science Press: Beijing, China, 2007, pp.37-38.
• Dossin E, Martin E, Diana P, et al.,“ Prediction Models of Retention Indices for Increased Confidence in Structural Elucidation during complex Matrix Analysis: Application to Gas Chromatography Coupled with High-Resolution Mass Spectrometry,” Analytical Chemistry, vol. 88, 2016, pp.7539-7547.
• Hammad, M. A., et al., “Myocardial Infarction Detection Based on Deep Neural Network on Imbalanced Data,” Multimedia Systems, vol.20,2021,p.728,.
• Zou Zhiwen, Zhu Jinwei, “Research and Review of Data Mining Algorithms,” Computer Engineering and Design, vol.26,2005, pp. 2304-2307,.
• Wang Huizhong, Peng Anqun, “Existing Situation of Data Mining Research and Its Development Tendency,” Industrial and Mining Automation, vol.2,2011, pp.29-32.
• Watson D S, Wright M N, “Testing conditional independence unsupervised learning algorithms,” Machine Learning, vol.110,2021, pp.2107-2129.
• Prahartiwi L I, Dari W., “Komparasi Algoritma Naive Bayes, Decision Tree dan Support Vector Machine untuk Prediksi Penyakit Kanker Payudara,” Journal Teknik Komputer,vol.7,2021,pp.51-54.
• RapidMiner Documentation, “compare_rocs. 2021.[Online]. Available:https://docs.rapidminer.com/latest/studi o/operators/validation/visual/compare_rocs.html.
• Zhu Fei, Yue Meiqi, Zhang Jiexuan, “The mediating Role of career satisfaction and the Moderating Role of employer brand,” Journal of Central University of Finance and Economics, vol.12, 2021, pp.105-118.
• Valentine S, Hollingworth D., “Communication of Organizational Strategy and Coordinated Decision Making as Catalysts for Enhanced Perceptions of Corporate Ethical Values in a Financial Services Company,” Employee Responsibilities and Rights Journal, vol.27,2015, pp.213-229.
• Hina Ghous， László Kovács., “Efficiency comparison of Python and RapidMiner,” Multidiszciplináris Tudományok, vol.10, 2020, pp.212-220.
• Moloud Abdar, “A Survey and Compare the Performance of IBMSPSS Modeler and Rapid Miner Software for Predicting Disease by Using Various Data Mining Algorithms,” Cumhuriyet Science Journal, vol.36, 2015, pp.3230-3241.
• IBM SPSS Modeler and RapidMiner Studio. Trust Radius, December 23, 2021.[Online]. Available:https://www.trustradius.com/comparepr oducts/ibm-spss-modeler-vs-rapidminer-studio.

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