Retain Edge: A Predictive Approach to Employee Retention

Abstract

In today’s competitive corporate landscape, employee retention is a critical challenge for organizations aiming to maintain productivity and reduce turnover costs. The project RetainEdge: A Predictive Approach to Employee Retention addresses this challenge by leveraging machine learning and predictive analytics to forecast employee attrition and provide data-driven insights into the factors influencing turnover. This system uses advanced techniques like Random Forest, Logistic Regression, and Gradient Boosting, combined with Principal Component Analysis (PCA), to enhance model accuracy and interpretability. By analyzing key employee data, the model predicts which employees are at risk of leaving, enabling organizations to implement targeted interventions to enhance retention. The project is deployed via a Flask-based graphical user interface (GUI), offering an interactive platform for organizations to visualize and respond to attrition risks. This study contributes to the field of predictive HR analytics by presenting a practical solution that combines data science with human resource management, ultimately helping organizations improve employee satisfaction, engagement, and retention.