Enhanced sales analysis: predictive insights from machine learning models using the XGboost regressor approach

In the contemporary retail landscape, major department stores and Big Marts meticulously track individual product sales data to anticipate future consumer demand and optimize inventory management. These extensive data repositories encompass a wealth of customer information and specific product attributes housed in a centralized data warehouse. By mining this data store, anomalies and overarching patterns are identified, forming the basis for predicting future sales volumes through advanced machine learning techniques tailored for retail establishments such as Big Mart. This paper introduces a predictive model utilizing the XGBoost Regressor approach to forecast sales for a company like Big Mart, demonstrating enhanced performance compared to existing models.