Abstract

Modern shopping centers and Big Marts track their sales data for each and every item to forecast client demand in the future and to maintain inventory control. These data stores are essentially a data warehouse with a lot of consumer data and individual item details. Furthermore, by mining the data store from the data warehouse, anomalies and recurring patterns are found. For shops like Big Mart, the resulting data can be utilized to forecast future sales volume with the use of various machine learning algorithms. For estimating the sales of a corporation like Big Mart, we present a predictive model in this research utilising the XG boost Repressor approach. We discovered that the model outperforms existing models