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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: | Prediction Of Employee Attrition Using Machine Learning |
| Authors Name: | Ayodhya Yasaswini , Neelapu Lokesh Kanaka Sai Reddy , K.Tarun Sai , K.M.V.V.Prasad |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_189549
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| Published Paper Id: | IJNRD2303367 |
| Published In: | Volume 8 Issue 3, March-2023 |
| DOI: | |
| Abstract: | Attrition is a term used to describe the process of reduction or decrease in the number of employees, customers, or participants over time. It can occur for a variety of reasons, including resignation, retirement, termination, or death. Employee attrition leads to a massive loss for the organization. This research study helps to predict employee attrition and helps HR Managers to understand the reason behind their employee’s attrition using a machine learning model. This research study contains two datasets (IBM HR dataset and Healthcare dataset) to predict the attrition of employees working in two different organizations. Random Forest Classifier algorithm was used to predict employee attrition, this approach attained an accuracy score of 89% for the IBM HR dataset and 95% for the Healthcare dataset. To determine the factors that contributed to employee attrition, an Employee Exploratory Data Analysis (EEDA) was conducted. The key factors behind employee attrition, as per our study, were found to be Business Travel, Department, Education Field, Gender, Job Role, Marital status, and Over Time. To simplify the model complexity, we employed the data resampling technique called Synthetic Minority Oversampling Technique (SMOTE) on both datasets to balance them. The proposed approach aims to assist organizations in addressing employee attrition by identifying and enhancing the factors that contribute to it. |
| Keywords: | Employee Attrition, Random Forest Classifier, Causes of Employee Attrition |
| Cite Article: | "Prediction Of Employee Attrition Using Machine Learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 3, page no.d503-d513, March-2023, Available :http://www.ijnrd.org/papers/IJNRD2303367.pdf |
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| 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:IJNRD2303367 Registration ID: 189549 Published In: Volume 8 Issue 3, March-2023 DOI (Digital Object Identifier): Page No: d503-d513 Country: Visakhapatnam, Andhra Pradesh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2303367 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2303367 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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