Artificial Intelligence-based Dynamic E-Commerce Pricing System using Machine Learning
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Abstract
Pricing is a key factor that directly affects sales and profit in e-commerce businesses. Many existing e-commerce platforms use fixed or manually updated prices, which do not respond quickly to changes in customer demand, competitor pricing, or market trends. This paper proposes an AI-Based Dynamic E-commerce Pricing System that automatically adjusts product prices using machine learning techniques. The system applies Random Forest Regression to analyze historical sales data, product demand, and competitor prices in order to predict the most suitable price for each product. SQL Server is used to store and manage product and sales data securely, while Power BI is integrated to provide clear and interactive visual dashboards showing pricing trends, profit, and loss. Experimental results indicate that the proposed system improves pricing accuracy, reduces manual effort, and supports better Decision-making. Overall, the system offers an efficient and intelligent pricing solution for modern e-commerce platforms.
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