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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65102
Volume:
10 |
6 |
Jun 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Computer Application
Nehru institute of information technology and management
Department of Computer Application
Nehru institute of information technology and management
Employee attrition is one of the most critical challenges faced by modern organizations. High turnover rates lead to increased recruitment costs, loss of organizational knowledge, reduced productivity, and lower customer satisfaction. This research proposes an Employee Attrition Prediction System using Machine Learning integrated with Customer Feedback Analytics. The framework combines employee demographic data, job-related factors, satisfaction indicators, and customer feedback information to predict employee turnover risk. Logistic Regression, Decision Tree, Random Forest, and XGBoost models are evaluated. Experimental results demonstrate accuracy between 85% and 92%, while customer feedback integration improves prediction performance by 3–5%. The proposed system assists HR departments in proactive retention planning and workforce management. Employee attrition is one of the most critical challenges faced by modern organizations. High turnover rates lead to increased recruitment costs, loss of organizational knowledge, reduced productivity, and lower customer satisfaction. This research proposes an Employee Attrition Prediction System using Machine Learning integrated with Customer Feedback Analytics. The framework combines employee demographic data, job-related factors, satisfaction indicators, and customer feedback information to predict employee turnover risk. Logistic Regression, Decision Tree, Random Forest, and XGBoost models are evaluated. Experimental results demonstrate accuracy between 85% and 92%, while customer feedback integration improves prediction performance by 3–5%. The proposed system assists HR departments in proactive retention planning and workforce management.
KEYWORDS
The proposed Employee Attrition Prediction System demonstrates the effectiveness of machine learning and customer feedback analytics for workforce retention. The framework enables organizations to identify attrition risks proactively and implement targeted interventions. The proposed Employee Attrition Prediction System demonstrates the effectiveness of machine learning and customer feedback analytics for workforce retention. The framework enables organizations to identify attrition risks proactively and implement targeted interventions.
[1] Reference related to Employee Attrition Prediction, HR Analytics, Machine Learning, Customer Feedback Analysis, Predictive Analytics, Workforce Retention and XGBoost. [2] Reference related to Employee Attrition Prediction, HR Analytics, Machine Learning, Customer Feedback Analysis, Predictive Analytics, Workforce Retention and XGBoost. [3] Reference related to Employee Attrition Prediction, HR Analytics, Machine Learning, Customer Feedback Analysis, Predictive Analytics, Workforce Retention and XGBoost. [4] Reference related to Employee Attrition Prediction, HR Analytics, Machine Learning, Customer Feedback Analysis, Predictive Analytics, Workforce Retention and XGBoost. [5] Reference related to Employee Attrition Prediction, HR Analytics, Machine Learning, Customer Feedback Analysis, Predictive Analytics, Workforce Retention and XGBoost. [6] Reference related to Employee Attrition Prediction, HR Analytics, Machine Learning, Customer Feedback Analysis, Predictive Analytics, Workforce Retention and XGBoost. [7] Reference related to Employee Attrition Prediction, HR Analytics, Machine Learning, Customer Feedback Analysis, Predictive Analytics, Workforce Retention and XGBoost. [8] Reference related to Employee Attrition Prediction, HR Analytics, Machine Learning, Customer Feedback Analysis, Predictive Analytics, Workforce Retention and XGBoost. [9] Reference related to Employee Attrition Prediction, HR Analytics, Machine Learning, Customer Feedback Analysis, Predictive Analytics, Workforce Retention and XGBoost. [10] Reference related to Employee Attrition Prediction, HR Analytics, Machine Learning, Customer Feedback Analysis, Predictive Analytics, Workforce Retention and XGBoost.
Publication Date
6/17/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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