Machine Learning-Based Price Prediction for Intelligent Retail Management
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Retail price prediction is an important component of intelligent retail analytics, supporting effective pricing strategies, revenue optimization, inventory planning, and data-driven decision-making. Because of the complexity and nonlinearity of large-scale retail data, traditional statistical methods may be limited. The Walmart Store Sales Forecasting Dataset is a dataset of 421,570 records with 16 variables, proposed for use in this study to develop a machine-learning predictive price analytics framework. The methodology includes missing-value treatment, duplicate removal, outlier management, one-hot encoding, Min–Max normalization, and SelectKBest feature selection. The dataset was divided into training and testing sets using an 80:20 ratio, and a Random Forest (RF) regression model was developed for prediction. The proposed RF model achieved an R-squared value of 98.96%, RMSE of 0.488, MAE of 0.317, and MSE of 2.386. These results demonstrate the effectiveness of the proposed approach in capturing complex retail sales patterns and producing accurate predictions. The framework can enable intelligent pricing, enhance retail management performance. In addition, the proposed approach can be scaled to process large retail data and gain insights into factors influencing sales performance. It can also help retailers make data-informed pricing decisions and optimize their operations in competitive markets.t)
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