Real-Time Adaptive Pricing in E-Commerce Using Deep Learning and Customer Demand Forecasting

Abstract

This project implements an intelligent dynamic pricing system for e-commerce platforms, designed to navigate the critical trade-off between maximizing business profitability and maintaining customer satisfaction. While traditional pricing strategies often fail to account for the temporal dependencies of market trends, this implementation leverages Long Short-Term Memory (LSTM) networks to process sequential e-commerce data. The system is deployed as a Flask-based web application that integrates real-time predictive modeling with a secure user management backend. By utilizing Stochastic Gradient Descent (SGD) for model optimization, the system achieves superior predictive accuracy, resulting in average profit margins of approximately 18.4% to 19.3% and high customer satisfaction scores. The results demonstrate that the LSTM-based approach effectively captures complex patterns in competitor pricing, demand, and customer behavior, providing a robust solution for modern retail environments.