DATA SCIENCE MEETS RETAIL: PREDICTING BIG MARTS’S SALES

In the modern era, shopping malls and Big Marts maintain a comprehensive record of their sales data for each individual item. This data is utilized to predict future customer demand and update inventory management. These records are stored in data warehouses, containing a vast amount of customer data and item attributes. By mining this data, anomalies and frequent patterns can be identified. The resulting information can be leveraged to forecast future sales volume using various machine learning techniques, specifically for retailers like Big Mart. In this study, we propose a predictive model that utilizes the XG boost Regressor technique to forecast sales for companies similar to Big Mart.It one the affordable method when comparative to other methods Our findings indicate that this model outperforms existing models in terms of performance.