Welcome to SmartSuggest! This project implements a personalized product recommendation system using machine learning techniques to enhance user experience and drive engagement.
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.
- Languages: Python, SQL
- Libraries/Frameworks: Scikit-learn, TensorFlow, Keras, Pandas, NumPy
- Tools: Jupyter Notebooks, VSCode
- 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.
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.
For any inquiries or feedback, please reach out via:
- Email: pradeepyadav809064@gmail.com
- LinkedIn: linkedin.com/in/pradeep31747
- GitHub: github.com/Pradeep31747