Explore the capabilities of a Hybrid Book Recommendation System in this project demo! 📖✨ We'll showcase how our system leverages collaborative filtering and content-based filtering to provide personalized book recommendations. Using Python and Streamlit, we've built a robust recommendation engine that processes data efficiently and offers diverse suggestions. Perfect for students, data enthusiasts, and anyone interested in data science and machine learning!
Key Highlights:
Introduction to the Hybrid Book Recommendation System
Dataset overview and key attributes
Backend implementation: Data preprocessing, model training, and hybrid approach
Frontend development with Streamlit
Evaluation metrics for recommendation systems
Advantages and use cases of hybrid recommendation systems
Conclusion and insights
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📂 GitHub Source Code:
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