Abstract
These days online shopping and MegaMarts record their sales and purchase data of each and every item. As the competition between various stores is increasing rapidly, it is necessary to predict future demand of each product at various stores for the customers. This data contain various attributes related to product like its ID, store ID, weight of product, visibility percentage of product, its fat content, its type, location of store, etc. This data are then analyzed to detect the further, anomalies and frequent patterns in the data. After analyzing data, it is processed so as to give us exact report for sales of each product. Then, final data can be used for predicting future sales using different machine learning techniques. We apply different machine learning models like ‘linear regression’, ‘decision tree’, ‘random forest’, ‘ridge regression’, and ‘XGBoost model’ to predict outlet sales. We found out that XGBoost gives us the best accuracy. With this predicted sales, MegaMart can observe the various patterns that should be changed to ensure its success in business.
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Gupta, G., Gupta, K.L., Kansal, G. (2023). MegaMart Sales Prediction Using Machine Learning Techniques. In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Rodrigues, J.J.P.C., Ganzha, M. (eds) Proceedings of Third International Conference on Computing, Communications, and Cyber-Security. Lecture Notes in Networks and Systems, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-19-1142-2_35
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DOI: https://doi.org/10.1007/978-981-19-1142-2_35
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