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With The Rapid Growth Of E-commerce And The Fashion Retail Industry, Automated Product Classification And Personalized Recommendations Have Become Essential For Improving User Experience And Increasing Sales. Bags, Being A Diverse Product Category With Variations In Design, Size, Material, And Style, Pose Significant Challenges For Accurate Classification And Effective Product Suggestions. Traditional Machine Learning Approaches Often Rely On Handcrafted Features, Which Are Limited In Capturing Complex Visual Patterns. To Address This, This Study Proposes A Chatbot System For Bag Classification And Product Suggestion Using EfficientNet, A State-of-the-art Deep Learning Architecture Known For Its High Accuracy And Computational Efficiency. The Proposed Model Leverages EfficientNet For Robust Feature Extraction And Fine-grained Classification Of Different Bag Categories, While The Chatbot Interface Enables Interactive Communication With Users To Provide Real-time Product Recommendations Based On Preferences And Browsing Behavior. This Integration Of Deep Learning With Conversational AI Not Only Enhances Classification Accuracy But Also Improves Customer Engagement And Decision-making In Online Shopping.