E-commerce-product-recommendation

This E-Commerce Product Recommendation System suggests relevant products to users using both content-based and collaborative filtering techniques. It analyzes product descriptions and user behavior to deliver personalized recommendations through a normal E-commerce style interactive Gradio UI. Sure! Here's a clean and professional README description for your E-Commerce Product Recommendation System:

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This project is a Flipkart-style Product Recommendation Engine built using Python and Gradio. It combines Content-Based Filtering and Collaborative Filtering to suggest relevant products to users based on:

• Product features (description similarity)
• User preferences (similar user behavior)

The system uses TF-IDF for analyzing product descriptions and a user-product rating matrix for collabortive filtering. The UI is designed using Gradio to simulate a simple and modern e-commerce experience.

🔍 Features

• 🎯 Content-Based Recommendations
  Suggest similar products based on selected item descriptions.

• 👥 Collaborative Filtering
  Suggest products by analyzing similar user behavior and preferences.

• 💻 Gradio Interface
  User-friendly UI mimicking Flipkart-style product cards with images and details.

📦 Technologies Used

• Python
• Gradio
• scikit-learn
• Pandas
• Cosine Similarity (for both users and products)

🚀 How to Run (on Google Colab)

1. Install dependencies:

   !pip install gradio scikit-learn

2. Paste the full code block in a Colab cell.

3. Launch and interact via the provided Gradio link.

📁 Dataset (Sample)

• A mini dataset of products with titles, descriptions, and images
• Sample user-product rating matrix for demo purposes

📌 Future Enhancements

• Real-time API integration with e-commerce platforms
• User login and preferences persistence
• Advanced recommendation algorithms (XGBoost, Deep Learning)