Advancing Retail Predictions: Integrating Diverse Machine Learning Models for Accurate Walmart Sales Forecasting

作者：Cyril Neba C., Gerard Shu F., Gillian Nsuh, Philip Amouda A., Adrian Neba F., F. Webnda, Victory Ikpe, Adeyinka Orelaja, Nabintou Anissia Sylla · 发表于：Asian Journal of Probability and Statistics · 年份：2024 · DOI：10.9734/ajpas/2024/v26i7626 · 被引用次数：9

In the rapidly evolving landscape of retail analytics, the accurate prediction of sales figures holds paramount importance for informed decision-making and operational optimization. Leveraging diverse machine learning methodologies, this study aims to enhance the precision of Walmart sales forecasting, utilizing a comprehensive dataset sourced from Kaggle. Exploratory data analysis reveals intricate patterns and temporal dependencies within the data, prompting the adoption of advanced predictive modeling techniques. Through the implementation of linear regression, ensemble methods such as Random Forest, Gradient Boosting Machines (GBM), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), this research endeavors to identify the most effective approach for predicting Walmart sales. Comparative analysis of model performance showcases the superiority of advanced machine learning algorithms over traditional linear models. The results indicate that XGBoost emerges as the optimal predictor for sales forecasting, boasting the lowest Mean Absolute Error (MAE) of 1226.471, Root Mean Squared Error (RMSE) of 1700.981, and an exceptionally high R-squared value of 0.9999900, indicating near-perfect predictive accuracy. This model's performance significantly surpasses that of simpler models such as linear regression, which yielded an MAE of 35632.510 and an RMSE of 80153.858. Insights from bias and fairness measurements underscore the effectiveness of adva...