Published Paper Details:

Sales forecasting is critical for businesses involved in retailing, shipping, manufacturing, marketing, and wholesaling. It enables businesses to deploy resources more efficiently, estimate sales revenue, and develop better strategies for the future. In this research, predicting product sales from a certain retailer is done in a method that outperforms any machine learning technique. This project makes use of Big Mart Sales data from 2013.Nowadays, shopping malls and supermarkets keep track of specific item sales data in order to forecast client demand in the future. It holds a vast amount of consumer information as well as item attributes. Furthermore, regular patterns are discovered through mining the data warehouse. The data can then be utilised to forecast future sales for organisations like Big Mart using various machine learning approaches (algorithms). In this research, we propose a model based on the Xgboost algorithm for predicting sales of enterprises such as Big Mart and show that it outperforms other existing models. This project compares this model against other models in terms of their performance measures. Big Mart is a low-cost internet marketplace where users may purchase, sell, or advertise their goods. The purpose of this article is to make Big Mart a shopping paradise for buyers as well as a marketing solution for merchants. The ultimate goal is complete customer pleasure. The project SUPERMARKET SALES PREDICTION creates a predictive model that determines the sales of each product at a specific store. This model is used by the Big Mart to underpin the properties of the products, which plays a significant part in raising sales. This can also be done on the basis of the hypothesis, which should be done prior to looking at the data.