Albuquerque, Carina Isabel AndradeCarneiro, Marta Francisco da Cunha Mendes2025-11-192025-11-192025-10-30http://hdl.handle.net/10362/191018Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Data Science for MarketingThis project develops a weekly merchandising sales forecasting model for a professional football club with the goals of maximizing order quantity from suppliers, avoiding stockouts, and reducing overstock. Based on the CRISP-DM process, historical sales, performance of the club, and weather information were collected, cleaned, and analysed. The extracted variables were used to depict product launches and promotions,so that the last dataset could be weekly aggregated and employed to train and compare the various forecasting models: SARIMAX, XGBoost, LightGBM, and Random Forest. XGBoost performed better than the other models, exhibiting the following performance, RMSE of 84.221, MAE of 46010.88 and an adjusted R² of 0.846, being superior in detecting non-linear relationships and intricate patterns in the data. This study demonstrates how machine learning methodology can become a major value driver of operational efficiency, enabling inventory management and creation of more strategic marketing campaigns, in addition to maximizing fan experience through access to most desirable products.engMachine LearningForecasting DemandTime seriesARIMAXGBoostRandom ForestLightGBMSDG 8 - Decent work and economic growthSDG 9 - Industry, innovation and infrastructureSDG 12 - Responsible production and consumptionSDG 17 - Partnerships for the goalsmaster thesis204070880