Belo, RodrigoSchirripa, Monica2025-01-022025-01-022024-01-102023-12-19http://hdl.handle.net/10362/176920This thesis is the result of my internship at The Data Cooks, an Amsterdam-based data agency, and aims to enhance demand forecasting for Triumph International, a leading lingerie manufacturer. The research is focused on addressing the complex challenge of disaggregated forecasting in the fashion retail industry, often characterized by a large number of stores and products. Central to this research is the application of gradient boosting algorithms, a cutting edge approach in machine learning. By focusing on methods such as LightGBM and XGBoost, this study delves into the effectiveness of ensemble learning in handling complex, time sensitive data.engDemand forecastingMachine learningTime-series forecastingFashion retailMaster thesisGradient boostingmaster thesis203681835