Exploratory Data analysis and sales forecasting of bigmart dataset using supervised and ANN algorithms. (October 2022)

• Record Type: Journal Article
• Authors: Thivakaran, T.K.; Ramesh, M.
• Is Part Of: Measurement. Volume 23(2022)
• Publication Date: 2022-10
• Subjects: ANN -- Machine learning -- Random forest -- Regression -- Sales forecast -- Xgboost; Detectors -- Periodicals; Measurement -- Periodicals; 530.7
• DOI: 10.1016/j.measen.2022.100388
• Languages: English
• ISSNs: 2665-9174

Abstract

Abstract: The essential concepts of sellers and buyers are supply and demand. Organizations must be able to accurately predict demand in order to establish plans. Sales Prediction is based on predicting sales for various Big Mart stores in order to adjust the business strategy based on the projected performance. For Big Mart firms, in this paper present a novel approach to demand forecast. The Big Mart firms' business model, for which the model is executed, contains multiple shops selling the same product at the same time across the country where the company maintains a marketplace model. This proposed Supervised and Artificial Neural Network Algorithms produces reliable results when compared to other learning methods. Feature selection, data transformation, and data exploration will all play essential roles. This method is used on data from Big-Mart Sales, where data is discovered, processed, and enough relevant data is taken to help forecast correct future outcomes.