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Employee Attrition Prediction Using Machine Learning Algorithms · Study

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ChapterEmployee Attrition Prediction Using Machine Learning Algorithms

Lok Sundar Ganthi2021

Chapter2021

Employee Attrition Prediction Using Machine Learning Algorithms

Lok Sundar Ganthi · Yaswanthi Nallapaneni · Perumalsamy Deepalakshmi · Krishnakumar Mahalingam

16 citations·0/0 files stored·Lecture notes in networks and systems

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AI and HR Technologies Online Learning and Analytics Customer churn and segmentation

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References

2016Prediction of Employee Turnover in Organizations using Machine Learning Algorithms 2018Employee Turnover Prediction with Machine Learning: A Reliable Approach 2019An Improved Random Forest Algorithm for Predicting Employee Turnover 2010Human Talent Prediction in HRM using C4.5 Classification Algorithm 2015A data mining approach to employee turnover prediction (case study: Arak automotive parts manufacturing)2008Establishing a link between employee turnover and withdrawal behaviours: application of data mining techniques 2016Error management culture and turnover intent among food and beverage employees in deluxe hotels: the mediating effect of job satisfaction 2018Employee Attrition Prediction

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2020Employee's Attrition Prediction Using Machine Learning Approacheschapter · 4 shared references 2019Mirror, Mirror on the Wall, Who Is Leaving of Them All: Predictions for Employee Turnover with Gated Recurrent Neural Networkschapter · 4 shared references 2025An integrated model to evaluate the transparency in predicting employee churn using explainable artificial intelligencearticle · 4 shared references 2021From Big Data to Deep Data to Support People Analytics for Employee Attrition Predictionarticle · 3 shared references 2023A decade of research on machine learning techniques for predicting employee turnover: A systematic literature reviewreview · 3 shared references 2023Retention is All You Needother · 3 shared references

Details

Year2021

Typechapter

Published2021-11-22

VenueLecture notes in networks and systems

ISSN-L2367-3370

Languageen

Citations16

Files0 public / 0

ShelfAI and HR Technologies

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doi10.1007/978-981-16-5120-5_44

openalex_workW3217601085

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