E-commerce Recommendation System

An intelligent full-stack e-commerce web application that provides personalized product recommendations using a hybrid recommendation engine and an AI-powered chatbot.

Live Demo

🔗 https://ecommerce-recommendation-system-8t1l.onrender.com/index

Features

• User-friendly e-commerce interface
• AI chatbot for real-time product suggestions
• Personalized recommendations using hybrid filtering
• Deployed and accessible online

Recommendation System

This project uses a Hybrid Recommendation Engine combining:

Collaborative Filtering

• Suggests products based on similar user's behaviour

Content-Based Filtering

• Recommends items based on product features and user preferences

AI Chatbot

• Powered by Groq API (LLaMA 3.3 70B)
• Understands user queries in natural language
• Provides context-aware product suggestions
• Enhances user interaction and experience

Tech Stack

Frontend

• HTML
• CSS
• JavaScript

Backend

• Python (Flask)

Database

• SQLite

Machine Learning

• Scikit-learn

AI Integration

• Groq API (LLaMA 3.3 70B)

Deployment

• Render

Project Structure

Ecommerce_Recommendation_System/

├── static/
├── templates/
├── app.py
├── models/
└── requirements.txt

Installation & Setup

Clone the repository

• git clone https://github.com/Pavithra-git162/Ecommerce_Recommendation_System.git
• cd Ecommerce_Recommendation_System

Install dependencies

• pip install -r requirements.txt

Run the application

• python app.py

Open in browser

• http://127.0.0.1:5000/

Future Enhancements

• Integration of payment gateway
• Advanced recommendation models using deep learning