Data Driven Price Recommendation System For Online Market

Abstract

The rapid growth of e-commerce platforms and online marketplaces has increased the need for intelligent pricing strategies that can help sellers maximize profit while maintaining market competitiveness. Traditional pricing methods mainly rely on manual analysis, fixed pricing rules, and market intuition, which are often inefficient in handling dynamic market conditions, customer behavior, and large-scale product data. To address these challenges, the project “Data Driven Price Recommendation System For Online Market” proposes an intelligent framework that uses data analytics and machine learning techniques to recommend optimal product prices for online marketplaces. The system collects historical sales data, customer preferences, competitor pricing, product demand, seasonal trends, and market conditions for analysis. Machine learning algorithms such as Linear Regression, Random Forest, Decision Trees, and Neural Networks are applied to identify pricing patterns and predict suitable product prices that maximize sales and profitability. The proposed system supports real-time price recommendation, demand forecasting, and market trend analysis, enabling sellers to make accurate and data-driven pricing decisions. Experimental results demonstrate that the system improves pricing accuracy, enhances customer satisfaction, increases business revenue, and provides scalable and intelligent pricing solutions for modern e-commerce environments.