MACHINE LEARNING TECHNIQUES FOR PREDICTING STORE SALES

Abstract

Large malls and numerous stores are gathering information on the sales of goods or items as a crucial step in forecasting future demand and inventory management in today's modern world. Sales prediction is important for stores and for this re. Predicting sales is crucial for defining an organization's future. We, in this paper, have analyzed how multiple factors of the stores can affect their sales and how their sales can be improved in future. The main goal of this paper is to predict the sales of “Womart” stores, and also how the factors like location, regions, store type affect their sales. Therefore, in order to achieve this, we have applied various machine learning models to obtain more accurate outcomes. Results are produced using several machine learning models and the model that produces the best accuracy i.e., XGBoost with 65.5% accuracy was used for prediction. These observations may then be used to make decisions to increase sales.