Understanding and Managing Employee Attrition: Strategies for Retaining Talent

In this study, we address the issue of employee attrition by employing a synergistic approach that amalgamates Power BI analytics and advanced machine learning techniques. Our methodology includes a meticulous analysis of historical employee data using Power BI, uncovering subtle attrition trends, influential variables, and pertinent demographic insights. Subsequently, we engineer a predictive machine learning model that forecasts forthcoming attrition patterns and proficiently classifies employees at high risk of departure. The model’s precision is meticulously refined through rigorous feature engineering and judicious algorithm selection like Decision Trees, Gaussian NB, Random Forest, XGBoost. To actualize these insights, we develop a user-friendly Flask web application that seamlessly integrates the analytical and predictive processes. This intuitive application empowers users to input employee details and promptly receive accurate attrition predictions. Our innovative amalgamation of analytics, machine learning, and user-centric web deployment establishes a comprehensive and efficient solution. By arming organizations with actionable insights and preemptive strategies, we significantly contribute to ameliorating employee retention, fostering engagement, and augmenting overall organizational efficacy.