Abstract: Nowadays shopping malls and D 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 D Mart. In this paper, we propose a predictive model using Xgboost technique for predicting the sales of a company like D Mart and found that the model produces better performance as compared to existing models. A comparative analysis of the model with others in terms performance metrics is also explained in details.The aim is to build a predictive model and find out the sales of each product at a particular store.Using this model, D Mart will try to understand the properties of products and stores which play a key role in increasing sales.
Keywords: D Mart Sales Prediction.
| DOI: 10.17148/IJARCCE.2022.11132