Global Superstores Sales Prediction and Data Visualization Using Power BI

Abstract

Sales forecasting is an important aspect 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 Global superstore sales prediction from Kaggle. Nowadays shopping malls and Supermarkets keep track of the sales data of each 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 machine learning 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.