An Extensive Analytical Approach on Human Resources using Random Forest Algorithm

An Extensive Analytical Approach on Human Resources using Random Forest Algorithm

  IJETT-book-cover           
  
© 2021 by IJETT Journal
Volume-69 Issue-5
Year of Publication : 2021
Authors : Swarajya lakshmi v papineni, A.Mallikarjuna Reddy, Sudeepti yarlagadda , Snigdha Yarlagadda, Haritha Akkineni
DOI :  10.14445/22315381/IJETT-V69I5P217

How to Cite?

Swarajya lakshmi v papineni, A.Mallikarjuna Reddy, Sudeepti yarlagadda , Snigdha Yarlagadda, Haritha Akkineni, "An Extensive Analytical Approach on Human Resources using Random Forest Algorithm," International Journal of Engineering Trends and Technology, vol. 69, no. 5, pp. 119-127, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I5P217

Abstract
The current job survey shows that most software employees are planning to change their job role due to high pay for recent jobs such as data scientists, business analysts and artificial intelligence fields. The survey also indicated that work life imbalances, low pay, uneven shifts and many other factors also make employees think about changing their work life. In this paper, for an efficient organisation of the company in terms of human resources, the proposed system designed a model with the help of a random forest algorithm by considering different employee parameters. This helps the HR department retain the employee by identifying gaps and helping the organisation to run smoothly with a good employee retention ratio. This combination of HR and data science can help the productivity, collaboration and well-being of employees of the organisation. It also helps to develop strategies that have an impact on the performance of employees in terms of external and social factors.

Keywords
Human Resources, Forest.

Reference
[1] https://www.kaggle.com/arashnic/hr-analytics-job-change-of-data-scientists
[2] A. Singh, N. Thakur and A. Sharma., A review of supervised machine learning algorithms, 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, (2016) 1310-1315.
[3] N. Amruthnath and T. Gupta., A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance, 5th International Conference on Industrial Engineering and Applications (ICIEA), Singapore, (2018) 355-361. doi: 10.1109/IEA.2018.8387124.
[4] I Setiawan et al 2020 IOP Conf. Ser.: Mater. Sci. Eng. 830 032001
[5] A.Mallikarjuna, B. Karuna Sree., Security towards Flooding Attacks in Inter Domain Routing Object using Ad hoc Network International Journal of Engineering and Advanced Technology (IJEAT), 8(3) (2019).
[6] V. Kakulapati, Kalluri Krishna Chaitanya, KolliVamsi Guru Chaitanya&PonugotiAkshay., Predictive analytics of HR - A machine learning approach, Journal of Statistics and Management Systems, 23(6) (2020) 959-969. DOI: 10.1080/09720510.2020.1799497
[7] Srinivasa Reddy, K., Suneela, B., Inthiyaz, S.,Kumar, G.N.S., Mallikarjuna Reddy, A., Texture filtration module under stabilization via random forest optimization methodology., International Journal of Advanced Trends in Computer Science and Engineering, 8(3) (2019).
[8] Dana Pessach, Gonen Singer, Dan Avrahami, Hila Chalutz Ben-Gal, ErezShmueli, Irad Ben-Gal., Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming, Decision Support Systems, (134) (2020) 113290. ISSN 0167-9236, https://doi.org/10.1016/j.dss.2020.113290.
[9] A. M. Reddy, V. V. Krishna, L. Sumalatha and S. K. Niranjan., Facial recognition based on straight angle fuzzy texture unit matrix, International Conference on Big Data Analytics and Computational Intelligence (ICBDAC), Chirala, (2017) 366-372. doi: 10.1109/ICBDACI.2017.8070865.
[10] IshaanBallal, ShlokKavathekar, ShubhamJanwe, Pratik Shete, Prof. NiveditaBhirud., People Leaving the Job – An Approach for Prediction Using Machine Learning , IJRAR - International Journal of Research and Analytical Reviews (IJRAR), E-ISSN 2348-1269, P- ISSN 2349-5138, 7(1) 891-893 (2020). Available at : http://www.ijrar.org/IJRAR2001987.pdf.
[11] A. M. Reddy, K. SubbaReddy and V. V. Krishna., Classification of child and adulthood using GLCM based on diagonal LBP, International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Davangere, (2015) 857-861. doi: 10.1109/ICATCCT.2015.7457003.
[12] A. Mottini and R. Acuna-Agost., Relative Label Encoding for the Prediction of Airline Passenger Nationality., IEEE 16th International Conference on Data Mining Workshops (ICDMW), Barcelona, Spain, (2016) 671-676. doi: 10.1109/ICDMW.2016.0100.
[13] Ayaluri MR, K. SR, Konda SR, Chidirala SR., Efficient steganalysis using convolutional auto encoder network to ensure original image quality, PeerJ Computer Science 7:e356 https://doi.org/10.7717/peerj-cs.356, (2021).
[14] Andronicus A. Akinyelu, Aderemi O. Adewumi., Classification of Phishing Email Using Random Forest Machine Learning Technique, Journal of Applied Mathematics, Article ID 425731, (2014) 6. https://doi.org/10.1155/2014/425731.
[15] ShabanShataee, SyavashKalbi, AsgharFallah& Dieter Pelz., Forest attribute imputation using machine-learning methods and ASTER data: comparison of k-NN, SVR and random forest regression algorithms, International Journal of Remote Sensing, 33(19) (2012) 6254-6280. DOI: 10.1080/01431161.2012.682661 .
[16] Necula, S.-C., and Strîmbei, C., People Analytics of Semantic Web Human Resource Résumés for Sustainable Talent Acquisition.Sustainability, 11(13) (2019) 3520.https://doi.org/10.3390/su11133520 .
[17] S. Yadav, A. Jain and D. Singh., Early Prediction of Employee Attrition using Data Mining Techniques, IEEE 8th International Advance Computing Conference (IACC), Greater Noida, India, (2018) 349-354. doi: 10.1109/IADCC.2018.8692137.
[18] Swarajya Lakshmi V Papineni, Snigdha Yarlagadda, Harita Akkineni, A. Mallikarjuna Reddy., Big Data Analytics Applying the Fusion Approach of Multicriteria Decision Making with Deep Learning Algorithms, International Journal of Engineering Trends and Technology, 69(1) 24-28. doi: 10.14445/22315381/IJETT-V69I1P204.