Comparative Evaluation of Machine Learning Models for Retail Sales Forecasting: A Multi-Algorithm Approach
Abstract: Accurate sales forecasting is vital for retail operations, impacting inventory management and strategic planning. This study explores the application of advanced machine learning models- Linear Regression, Random Forest, XGBoost, Support Vector Regression, LSTM, and ARIMA to improve forecasting accuracy. A five-year dataset of retail transactions was preprocessed using differencing and lag features to enhance stationarity. Among all models tested, XGBoost demonstrated the highest predictive accuracy (R?: 0.989), outperforming traditional methods. The research provides a scalable framework for real-time forecasting, underlining machine learning's transformative role in the retail sector and offering practical implications for inventory optimization and customer demand prediction.
Keywords: retail forecasting, machine learning, XGBoost, sales prediction, time series analysis
How to Cite?: Mayank Dwivedi, Supriya Mishra, "Comparative Evaluation of Machine Learning Models for Retail Sales Forecasting: A Multi-Algorithm Approach", Volume 14 Issue 6, June 2025, International Journal of Science and Research (IJSR), Pages: 1122-1134, https://www.ijsr.net/getabstract.php?paperid=SR25601090618, DOI: https://dx.doi.org/10.21275/SR25601090618