https://github.com/okaditya84/smart-product-recommender
SmartProductRecommender is an advanced recommendation system that uses purchase history data to provide precise and relevant product suggestions. It employs sophisticated similarity calculations and dynamic price adjustments to enhance user shopping experiences.
https://github.com/okaditya84/smart-product-recommender
ai data-science python recommendation-system statistics
Last synced: about 2 months ago
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SmartProductRecommender is an advanced recommendation system that uses purchase history data to provide precise and relevant product suggestions. It employs sophisticated similarity calculations and dynamic price adjustments to enhance user shopping experiences.
- Host: GitHub
- URL: https://github.com/okaditya84/smart-product-recommender
- Owner: okaditya84
- License: mit
- Created: 2025-01-25T15:41:36.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-25T15:56:41.000Z (over 1 year ago)
- Last Synced: 2025-03-30T18:45:04.857Z (about 1 year ago)
- Topics: ai, data-science, python, recommendation-system, statistics
- Language: Jupyter Notebook
- Homepage:
- Size: 1.03 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SmartProductRecommender
## Overview
SmartProductRecommender is a robust recommendation system that leverages purchase history data to provide accurate and relevant product suggestions. It utilizes advanced similarity calculations and dynamic price range adjustments to enhance user shopping experiences.
## Features
- **Data Preprocessing**: Cleans and processes sales and customer data to ensure consistency and accuracy.
- **Purchase Pattern Analysis**: Analyzes purchase patterns to extract meaningful insights such as median quantity, average price, and unique customers.
- **Similarity Calculation**: Calculates product similarity based on price, purchase patterns, and customer overlap.
- **Dynamic Price Range Calculation**: Adapts price ranges dynamically based on product price volatility.
- **Recommendation Generation**: Provides product recommendations based on similarity scores, confidence scores, and fallback mechanisms.
- **API Integration**: Includes a Flask API to serve recommendations based on user input.
## Installation
1. **Clone the repository**:
```bash
git clone https://github.com/yourusername/SmartProductRecommender.git
cd SmartProductRecommender
2. **Install the dependencies**:
```bash
pip install -r requirements.txt
3. **Run the application**:
```bash
python app.py
## Usage
## API Usage
### Get Recommendations
- **Endpoint**: `GET /api/recommend`
- **Description**: Retrieve product recommendations
- **Parameters**:
- `product` (string): Target product name
- **Example Request**:
```http
GET /api/recommend?product=laptop
```
- **Example Response**:
```json
{
"recommendations": [
{
"product": "Product Name",
"similarity_score": 0.95,
"avg_price": 100.0,
"typical_quantity": 10,
"confidence_score": 0.90
},
...
]}
```
## Example usage in juypyter notebook
```python
from product_recommendation import ProductRecommender
# Initialize the recommender
recommender = ProductRecommender()
recommender.load_and_process_data('SALE DATA.csv', 'CUSTOMER DATABASE.csv')
# Get recommendations
input_product = "Lux White FlawlessGlow (PO4)41Gm(40*54)"
recommendations = recommender.recommend_products(input_product, price=28.41, quantity=3240)
# Print recommendations
print(f"Recommendations for '{input_product}':")
for idx, rec in enumerate(recommendations, 1):
print(f"{idx}. {rec['product']} (Avg Price: ₹{rec['avg_price']:.2f}, Typical Quantity: {rec['typical_quantity']})")
```
## Project Structure
- app.py: Flask API implementation to serve recommendations.
- product_recommendation.py: Core recommendation logic and data processing.
- requirements.txt: List of dependencies required to run the project.
- productRecommendation_purchaseHistory.ipynb: Jupyter Notebook for interactive analysis and testing.
## Contributing
Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.
## License
This project is licensed under the MIT License. See the LICENSE file for details.