Supermarket Sales Prediction Using Regression

Abstract

Sales forecasting is an important when it comes to companies who are engaged in retailing, logistics, manufacturing, marketing and wholesaling. It allows companies to allocate resources efficiently, to estimate revenue of the sales and to plan strategies which are better for company’s future. In this paper, predicting product sales from a particular store is done in a way that produces better performance compared to any machine learning algorithms. The dataset used for this project is Big Mart Sales data of the 2013.Nowadays shopping malls and Supermarkets keep track of the sales data of the each and every individual item for predicting the future demand of the customer. It contains large amount of customer data and the item attributes. Further, the frequent patterns are detected by mining the data from the data warehouse. Then the data can be used for predicting the sales of the future with the help of several machine learning techniques (algorithms) for the companies like Big Mart. In this project, we propose a model using the Xgboost algorithm for predicting sales of companies like Big Mart and founded that it produces better performance compared to other existing models. An analysis of this model with other models in terms of their performance metrics is made in this project. Big Mart is an online marketplace where people can buy or sell or advertise your merchandise at low cost. The goal of the paper is to make Big Mart the shopping paradise for the buyers and a marketing solutions for the sellers as well. The ultimate aim is the complete satisfaction of the customers. The project “SUPERMARKET SALES PREDICTION” builds a predictive model and finds out the sales of each of the product at a particular store. The Big Mart use this model to under the properties of the products which plays a major role in increasing the sales. This can also be done on the basis hypothesis that should be done before looking at the data