Employee Attrition Prediction Using Machine Learning with Interactive Analytical Insights

Employee attrition is a major concern for organizations as it leads to increased recruitment costs and loss of skilled talent. Predicting employee attrition in advance can help organizations take proactive measures to improve retention. This work presents a machine learning–based system for predicting employee attrition using historical human resource data. The dataset is cleaned, analyzed, and transformed through feature engineering and scaling techniques before training the prediction model. The trained model estimates the probability of employee attrition and identifies key factors influencing the prediction. To make the system practical and user-friendly, the model is integrated into an interactive Streamlit application that supports individual prediction, bulk prediction, analytics dashboards, and model insights. The system also provides explanatory insights and HRoriented recommendations to support decision-making. The proposed approach demonstrates how machine learning combined with interactive analytics can assist organizations in understanding attrition patterns and improving employee retention strategies.