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ABSTRACT
Nowadays shopping malls and Big Marts keep the track of their
sales data of each and every individual item for predicting future
demand of the customer and update the inventory management
as well. These data stores basically contain a large number of
customer data and individual item attributes in a data warehouse.
Further, anomalies and frequent patterns are detected by mining
the data store from the data warehouse. The resultant data can
be used for predicting future sales volume with the help of
different machine learning techniques for the retailers like Big
Mart. In this paper, we propose a predictive model using XG
boost Regressor technique for predicting the sales of a company
like Big Mart and found that the model produces better
performance as compared to existing models.
