Ji, RongjiaoGuasti, Tommaso2025-02-112025-02-112024-06-212024-06-11http://hdl.handle.net/10362/178808This study develops a machine learning model to enhance inventory management at the “Kilt” clothing store by accurately predicting annual product sales, preventing overstock, and maximizing profitability. Analyzing historical sales data from 2009 to 2023, I evaluated multiple regression models, identifying the Random Forest as the most effective. The model forecasts 2024 profits to reach 2.2 million, significantly higher than 2023’s 1.4 million. By leveraging past sales data and advanced predictive modeling, the study provides strategic insights to optimize inventory decisions and improve overall store profitability.engFashion retailMachine learningDemand forecastingData analyticsData-driven decision makingmaster thesis203864530