Machine Learning Framework for Employee Retention Intelligence

Abstract

Machine Learning Framework for Employee Retention Intelligence presents a datadriven approach for identifying the factors that influence employee retention and predicting whether employees are likely to remain with an organization. The proposed framework utilizes a real-world human resource dataset containing employee demographic, professional, and workplace-related attributes to discover patterns associated with retention. Initially, the dataset undergoes preprocessing, including data cleaning, handling missing values, feature encoding, and normalization, to improve data quality and model performance. Several machine learning classification algorithms are trained and evaluated to determine the most effective model for employee retention prediction. Correlation analysis is also performed to identify the key factors that have the greatest impact on employee retention. The prediction models are assessed using standard evaluation metrics such as accuracy, precision, recall, and F1-score to ensure reliable performance. The developed framework assists organizations in identifying employees who may be at risk of leaving and supports human resource departments in making informed decisions regarding workforce planning, training investments, and employee engagement strategies. Overall, the proposed system provides an intelligent and practical solution for improving employee retention, reducing turnover, and supporting sustainable organizational growth.