Abstract: Retail sales prediction plays a crucial role in effective inventory management, marketing strategy, and business profitability. This research focuses on predicting the sales of Big Mart outlets using various machine learning techniques. The dataset contains information on different products and store attributes. We compare algorithms such as Linear Regression, Random Forest, and XGBoost to determine the best-performing model for accurate sales forecasting. The results show that ensemble-based methods outperform traditional regression models in prediction accuracy.
Keywords: Sales Prediction, Big Mart, Machine Learning, Random Forest, Regression, Retail Analytics
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DOI:
10.17148/IJARCCE.2025.141024
[1] Mr. Pavan Harilal Sonawane, Prof. Manoj Vasant Nikum*, "“Big Mart Sales Prediction Using Machine Learning”," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2025.141024
