🛍️ E-Commerce Product Recommender System

This is an intelligent product recommendation system for an e-commerce platform. Built using Python, Pandas, Scikit-learn, and Streamlit, it suggests similar products based on the user's selection using NLP and cosine similarity.

🔗 Developed as part of the Micro IT Internship Project.

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🚀 Features

• ✅ View detailed information of selected products
• 🤖 Get intelligent product recommendations
• 🖼️ Display product images dynamically
• 📦 User-friendly web interface built with Streamlit
• 📊 Content-based filtering using product metadata
• 💾 Runs locally in a browser

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📂 Project Structure

ecommerce_recommender/ ├── app.py # Streamlit App ├── recommender.py # Recommendation Engine Logic ├── products.csv # Product Dataset ├── images/ # Product Images ├── requirements.txt # Required Python Libraries ├── LICENSE └── README.md

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🧠 How It Works

The recommendation engine uses content-based filtering by vectorizing product metadata (title, description, category) using TF-IDF and calculates similarity using cosine similarity.

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🔧 Installation & Run

1. Clone the repository

   git clone https://github.com/your-username/ecommerce-recommender.git
   cd ecommerce-recommender
   

pip install -r requirements.txt streamlit run app.py

✅ Requirements

Python 3.8+

Streamlit

Pandas

Scikit-learn

Install with:
pip install -r requirements.txt

📄 License

This project is licensed under the MIT License. See the LICENSE file for details. 🙋‍♂️ Author

Bhooma Anand B.Tech CSE (AI), UTD CSVTU Bhilai 💼 Internship @ Micro IT 📬 razzanand97@gmail.com ⭐️ Show Your Support

If you like this project, give it a ⭐️ on GitHub — it helps others discover it!