OPTIMIZING REAL-TIME DYNAMIC PRICING STRATEGIES IN RETAIL AND E-COMMERCE USING MACHINE LEARNING MODELS

This study investigates the application of machine learning models for real-time dynamic pricing strategies in the retail and e-commerce sectors. We employed three prominent supervised machine learning models—Linear Regression, Random Forest, and Gradient Boosting Machines (GBM)—to predict optimal prices using a dataset sourced from Kaggle. The models were trained and evaluated with a 70:30 train-test split, while hyperparameter tuning was performed using grid search and cross-validation. The results indicate that the Gradient Boosting Machines (GBM) model consistently outperformed the other models, achieving the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), and demonstrating a higher R-squared (R²) value. The comparative analysis highlights GBM's ability to capture complex interactions in dynamic pricing data, making it a robust choice for accurate price forecasting. The Random Forest model also delivered satisfactory results, balancing accuracy and computational efficiency, whereas the Linear Regression model showed higher prediction errors due to its limitations in modeling non-linear relationships. Real-time testing in a simulated environment confirmed the models' adaptability and responsiveness in a dynamic marketplace. These findings provide actionable insights for retail and e-commerce businesses, emphasizing the importance of model selection, hyperparameter optimization, and system integration to implement efficient dynamic pricing strategies. Future work should explore more extensive datasets and real-world applications to address seasonal variations, regional preferences, and consumer behavior, ensuring a more comprehensive and practical deployment of machine learning-driven dynamic pricing models.