This project, part of a 6-week internship with SkillsBuild and Edunet Foundation (in collaboration with AICTE), focuses on predicting employee burnout using regression techniques. The dataset includes factors like workload, mental fatigue, and WFH status. Key Steps: Data Preprocessing: Handled missing values, encoded categorical features, and scaled data. EDA: Analyzed patterns and correlations. Model Development: Applied regression models (Linear, Ridge, Lasso) with cross-validation. Evaluation: Assessed performance using MAE, RMSE, and R². The model effectively predicts burnout risk, helping organizations enhance employee well-being.