Dropshipping Using Current Market Analysis

This project presents a data-driven dropshipping recommendation system that leverages deep learning techniques to provide real-time, personalized product suggestions based on market analysis and customer feedback. Unlike traditional approaches that rely on static data, this system integrates GPT-based price trend analysis and BERT-based sentiment analysis to identify products with both high market demand and positive customer reviews. The GPT model analyzes price trends to classify products within desirable price ranges, while the BERT model evaluates customer feedback to filter out products with poor sentiment. The combined output from these models is used to select the most promising products, which are then displayed on a custom-built, user-friendly website. This integrated approach ensures that the dropshipping business can adapt to changing market trends and customer preferences, offering a curated selection of products that enhance customer satisfaction and drive sales. The system thus provides an efficient solution for dropshippers seeking to improve product selection, customer engagement, and overall business growth.