employee attrition prediction using data driven machine learning models
Soumya Babasaheb Kudache
, Neha N , Rakshitha S , Prof Poornima Gowda
Employee Attrition, Retention, Predictive Modeling, Machine Learning, HRM, Data-Driven Strategies
Attrition of employees is a critical problem for businesses,
affecting productivity, operational expenses, and morale among
employees. Proactive retention and efficient HR management
can be implemented by companies when attrition can be
accurately forecasted. The current study examines the use of
advanced machine learning models and data analysis in the
prediction of employee turnover. The suggested approach finds
key drivers of attrition based on organizational data such as
demographic data, performance data, levels of job satisfaction,
and history of turnover. Through the utilization of this system,
HR departments are able to gain actionable information,
minimize worker turnover, and develop a more stable and
motivated workforce. The results emphasize how data-driven
policies can transform the management of people and create
organizational sustainability.
"employee attrition prediction using data driven machine learning models", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 3, page no.a513-a517, March-2025, Available :https://ijsdr.org/papers/IJSDR2503057.pdf
Volume 10
Issue 3,
March-2025
Pages : a513-a517
Paper Reg. ID: IJSDR_300810
Published Paper Id: IJSDR2503057
Downloads: 000158
Research Area: Science and Technology
Country: bengaluru, Karnataka, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
