This project demonstrates a Product Recommendation System built with Python, leveraging advanced NLP models like Sentence Transformers to provide accurate recommendations based on product descriptions. The system is designed to enhance e-commerce platforms by suggesting relevant products to users based on their search queries.
- Sentence Transformers: Utilizes Sentence Transformers to convert textual data into numerical embeddings.
- Cosine Similarity: Calculates the similarity between the user's query and product descriptions to find the most relevant products.
- Interactive Streamlit App: Provides a user-friendly interface to input search queries and view recommendations in real-time.
- Image Fetching: Fetches product images from Google based on the product name.
- Python 3.7 or higher
- Streamlit
- pandas
- sentence-transformers
- requests
- BeautifulSoup4
- Pillow
pip install streamlit pandas sentence-transformers requests beautifulsoup4 pillow-
Clone the Repository
git clone https://github.com/alisufyan143/Product-Recommendation-System.git
-
Prepare the Dataset Ensure you have your dataset in the same directory or update the path in the code. For example, a CSV file named
sample-data.csvwith columnsid,name, anddescription. -
Run the Streamlit App
streamlit run app.py
This will start a local server and open the app in your default web browser.
The app allows users to enter a search query, such as "Game Controller" and receive product recommendations based on the descriptions in the dataset.
Explore the project interactively on Google Colab: https://colab.research.google.com/drive/1W4vrDXWyHRN8dwSgvzUolpSUZDriiYoz?usp=sharing
Feel free to fork this repository, submit issues, and make pull requests. All contributions are welcome!
This project is licensed under the MIT License.
For any questions or feedback, please open an issue or contact me directly.
Thank you for checking out this project! Happy coding! 🚀