https://github.com/pradeep31747/smartsuggest-personalized_product_recommendations

This project implements a personalized product recommendation system using machine learning techniques to enhance user experience and drive engagement.
https://github.com/pradeep31747/smartsuggest-personalized_product_recommendations

jupyter-notebook keras numpy pandas pyhton scikit-learn sql tensorflow vscode

Last synced: 3 days ago
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This project implements a personalized product recommendation system using machine learning techniques to enhance user experience and drive engagement.

• Host: GitHub
• URL: https://github.com/pradeep31747/smartsuggest-personalized_product_recommendations
• Owner: Pradeep31747
• Created: 2024-07-27T12:46:28.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-07-27T12:54:08.000Z (3 months ago)
• Last Synced: 2024-10-10T08:23:00.794Z (27 days ago)
• Topics: jupyter-notebook, keras, numpy, pandas, pyhton, scikit-learn, sql, tensorflow, vscode
• Language: Python
• Homepage:
• Size: 6.84 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# SmartSuggest - Personalized Product Recommendations

Welcome to SmartSuggest! This project implements a personalized product recommendation system using machine learning techniques to enhance user experience and drive engagement.

## Project Overview

SmartSuggest provides personalized product recommendations based on user preferences and behavior. The system leverages machine learning algorithms to deliver relevant product suggestions, improving user satisfaction and increasing sales.

## Technologies Used

- **Languages**: Python, SQL

- **Libraries/Frameworks**: Scikit-learn, TensorFlow, Keras, Pandas, NumPy

- **Tools**: Jupyter Notebooks, VSCode

## Features

- **Recommendation Algorithms**:

  - Collaborative Filtering: Recommends products based on user similarities.

  - Content-Based Filtering: Suggests products based on item features.

  - Hybrid Methods: Combines both approaches for enhanced accuracy.

- **Real-Time Recommendations**: Provides instant suggestions based on user interactions.

- **Scalability**: Handles large datasets and scales with growing user and product databases.

## Deployment

To deploy the recommendation system, integrate the trained model into your web or mobile application using the provided API endpoints or create a frontend interface for interaction.

## Contact

For any inquiries or feedback, please reach out via:

- Email: [email protected]

- LinkedIn: linkedin.com/in/pradeep31747

- GitHub: github.com/Pradeep31747