https://github.com/shaadclt/product-recommendation-system
This is a simple product recommendation system built using Python and Streamlit. The application provides product recommendations based on similarity scores and displays them in a visually appealing format.
https://github.com/shaadclt/product-recommendation-system
recommendation-system
Last synced: 6 months ago
JSON representation
This is a simple product recommendation system built using Python and Streamlit. The application provides product recommendations based on similarity scores and displays them in a visually appealing format.
- Host: GitHub
- URL: https://github.com/shaadclt/product-recommendation-system
- Owner: shaadclt
- License: mit
- Created: 2024-08-07T16:52:47.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-13T05:35:42.000Z (11 months ago)
- Last Synced: 2025-04-10T01:10:45.428Z (6 months ago)
- Topics: recommendation-system
- Language: Python
- Homepage:
- Size: 1.06 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# Product Recommendation System
This is a simple product recommendation system built using Python and Streamlit. The application provides product recommendations based on similarity scores and displays them in a visually appealing format.
## Features
- Select a product from the dropdown menu to get recommendations.
- Display top 3 recommended products with images.
## Getting Started
### Prerequisites
1. Python
2. Streamlit
3. Pandas
4. NumPy
5. Pickle
### Installation
1. Clone the repository:
```bash
git clone https://github.com/shaadclt/Product-Recommendation-System.git
cd Product-Recommendation-System
```
2. Install the required packages:
```bash
pip install -r requirements.txt
```
3. Ensure you have the `data.pkl` and `similarity.pkl` files in the root directory. These files should contain your dataset and the similarity matrix, respectively.
### Usage
1. Run the Streamlit application:
```bash
streamlit run app.py
```
2. Open your web browser and navigate to the URL displayed in the terminal (usually **http://localhost:8501**).
3. Select a product from the dropdown menu and click the "Recommend" button to see the recommendations.
### Example
The application recommends products based on their similarity scores and displays the top 3 similar products in a row.
## License
This project is licensed under the [MIT License](LICENSE.txt).