Authors: Balla Revathi, Dhavala Shilpa, Yerrapatruni Jagadeesh Kumar
Abstract: Accurate product pricing has become a critical requirement for modern e-commerce platforms due to rapidly changing market conditions, customer preferences, competitor strategies, and fluctuating product demand. Traditional pricing methods often rely on static rules and historical analysis, making them ineffective in responding to real-time market dynamics. To address these challenges, this project proposes an intelligent framework called Adaptive Commerce Intelligence Framework for Real-Time Product Value Forecasting Using Hybrid Predictive Learning Models, which integrates machine learning techniques with business intelligence analytics to support intelligent pricing decisions and real-time product value forecasting.The proposed system collects and analyzes various pricing-related parameters, including product base cost, competitor pricing, sales volume, stock availability, customer ratings, reviews, and market trends. Individual machine learning algorithms such as Linear Regression, Random Forest, Support Vector Machine (SVM), and XGBoost are initially trained and evaluated independently to assess their forecasting capabilities. These models are then combined into a Hybrid Predictive Learning Model that leverages the strengths of each algorithm to improve prediction accuracy, forecasting stability, and pricing adaptability.Random Forest and XGBoost effectively identify complex market patterns and pricing trends, while SVM captures non-linear relationships among pricing factors. Linear Regression contributes to understanding pricing dependencies and improving model consistency. The framework also incorporates real-time analytics, competitor monitoring, historical prediction tracking, interactive dashboards, and MySQL-based data management to enhance business intelligence and decision-making capabilities.Experimental analysis demonstrates that the proposed hybrid framework provides more accurate and reliable pricing forecasts compared to standalone machine learning approaches. By integrating predictive learning with adaptive commerce analytics, the system enables dynamic pricing optimization, improves market responsiveness, supports revenue growth, and enhances competitiveness in modern digital commerce environments.
DOI: http://doi.org/
