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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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| Paper Title: | HR Analytics for Predicting Employee Attrition: A Hybrid Algorithmic Approach |
| Authors Name: | Dr. K. Suvarchala Rani |
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IJRTI_210063
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| Published Paper Id: | IJRTI2602138 |
| Published In: | Volume 11 Issue 2, February-2026 |
| DOI: | |
| Abstract: | Abstract In a data-driven business landscape, predictive HR analytics plays a strategic role in managing workforce stability. The employee attrition, a long-standing issue in IT exists because in-house knowledge-based functions and employee mobility are threats to the company’s competitiveness. Traditional descriptive techniques give hardly any insights and organizations remain more reactive than proactive. This paper applies a hybrid HR analytics algorithmic framework, that draws from the Kaggle HR attrition data (n = 1,470) and validates the model on IT employee data from India (n = 102), demonstrating that the approach proved methodologically robust and practical feasibility. The predictive algorithms and model construction were carried out using Logistic Regression and Random Forest models, developed and analyzed based on prediction accuracy, interpretability, and generalizability. Based on the Kaggle dataset, Random Forest provided perfect scores to the data, though when it was used for IT workers, it did not generalize, indicating that the model was overfitting. Logistic Regression was less precise initially but generalized better and provided interpretable insight suitable for HR decision-making. Salary, tenure (especially in the first three years), overtime, job satisfaction, and age are stronger predictors of attrition. Younger employees and staff earlier in their careers were also more likely to leave, highlighting the generational and career stage dynamics. According to this study, attrition is influenced by financial indicators, career, lifestyle, and demographic components. Predictive analytics, and its application in HR practice, can provide evidence-based insights for proactive retention strategies in the IT sector Keywords: HR Analytics, Employee Attrition, Predictive Modelling, Logistic Regression, Random Forest, Hybrid Model Validation |
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| Cite Article: | "HR Analytics for Predicting Employee Attrition: A Hybrid Algorithmic Approach", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 2, page no.b284-b297, February-2026, Available :http://www.ijrti.org/papers/IJRTI2602138.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2602138
Registration ID:210063
Published In: Volume 11 Issue 2, February-2026
DOI (Digital Object Identifier):
Page No: b284-b297 Country: Secunderabad, Telangana, India Research Area: Management Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2602138 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2602138 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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