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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Recommendation System to Mitigate Attrition Risks in IT organizations using Machine Learning |
| Authors Name: | Kishore Kumar K , Selvakumar G , Harini S , Priyanka G |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_219368
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| Published Paper Id: | IJNRD2404746 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | In the ever-evolving landscape of organizational management, the conservation of human resources stands out as a paramount concern, elevating employee attrition to the forefront of strategic agendas. The complexities of attrition, arising from various causes, present challenges for HR managers and department leaders striving to proactively identify these signs. The widespread consequences of attrition, including disruptions in ongoing tasks, re-employment costs, and potential compromise of core technologies, underscore the urgency of our study. Our research introduces an innovative approach, proposing a predictive model that leverages machine learning algorithms to foresee employee attrition. This model assesses 35 variables, meticulously evaluating their impact on attrition and identifying significant contributors to the phenomenon. Variables such as environmental satisfaction, overtime work, and relationship satisfaction are highlighted as key contributors. Given the multifaceted repercussions of employee attrition, a comprehensive investigation into its causes and the development of an effective predictive framework become imperative. Our research delves into organizational factors influencing attrition, utilizing advanced machine learning algorithms for in-depth analysis. Departing from traditional methods, our approach adopts a holistic learning framework, deliberately omitting specific algorithmic mentions.The study reveals crucial contributors to attrition, including factors like monthly income, hourly rate, job level, and age. By pioneering this novel approach, organizations gain strategic insight to enhance factors contributing to attrition, fostering a more stable, resilient, and harmonious workforce environment. This innovative framework marks a paradigm shift in addressing employee attrition. |
| Keywords: | Machine learning, Supervised Learning, Logistic Regression., Attrition, Recommendation System |
| Cite Article: | "Recommendation System to Mitigate Attrition Risks in IT organizations using Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.h389-h399, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404746.pdf |
| Downloads: | 00025 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 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:IJNRD2404746 Registration ID: 219368 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: h389-h399 Country: Coimbatore, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404746 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404746 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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