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@article{198057,
        author = {Sarvesh Koparde and Prathamesh Patil and Rohan Vaswani and Abhishek Singh},
        title = {Dynamic Pricing using Machine Learning: A Hybrid Demand Prediction and Revenue Optimization Framework},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {8242-8247},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198057},
        abstract = {Dynamic pricing denotes the process of altering the prices of products on an ongoing basis according to demand changes, competition, and other factors. Static pricing models do not account for the variation in prices and often result in poor decision-making with regards to maximizing profits. This paper introduces a dynamic pricing model, based on machine learning techniques, that allows predicting demands and setting optimal prices accordingly. Demand predictions can be made using three algorithms—Linear Regression, Random Forest, and XGBoost—and an optimal price can be determined for maximized revenue. Once the predicted demand is calculated, a revenue optimization model is defined for each product. Experiments are carried out on a synthetic yet realistic dataset consisting of 15,000 transactions of seven different products. Our analysis reveals that our XGBoost-based pricing framework has the best prediction accuracy (R² = 0.93; RMSE = 21.47) and provides a 52.83% improvement over static pricing revenues. Also, we have observed that the Random Forest method provides a 43.26% boost in revenue gains. Therefore, the study indicates the applicability of ensemble machine learning algorithms in pricing solutions.},
        keywords = {Dynamic Pricing, Machine Learning, Demand Prediction, XGBoost, Random Forest, Revenue Optimization, Price Elasticity, E-commerce},
        month = {April},
        }