The Book Recommendation System is designed to provide personalized book suggestions to users based on their preferences and past interactions. Using popular-based filtering and collaborative filtering, the system helps users discover books they may enjoy. The project follows a modular coding approach, making it scalable and maintainable.
- Programming Language: Python
- Framework: Streamlit (for web app development)
- Database: SQLite3 (for storing user interactions and book metadata)
- Machine Learning Concepts:
- Popular-Based Filtering: Recommends books based on their overall popularity.
- Collaborative Filtering: Suggests books based on user behavior and similarities between users.
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Data Collection & Processing:
- Gather book metadata, ratings, and user interactions.
- Clean and preprocess the dataset for better efficiency.
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Model Development:
- Popular-Based Filtering:
- Uses statistical methods to recommend the most popular books across users.
- Collaborative Filtering:
- Uses user-item interactions to suggest books based on similar users’ reading habits.
- Popular-Based Filtering:
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Web Application Development:
- Built using Streamlit to provide an interactive UI.
- Users can search books, view recommendations, and rate books.
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Modular Coding Structure:
- Separate modules for data handling, model training, recommendation logic, and UI components.
- Ensures easy scalability and maintainability.
- A user-friendly web app where users can get personalized book recommendations.
- Efficient and scalable modular architecture.
- Improved recommendation accuracy using hybrid filtering techniques.
- Implement content-based filtering to improve recommendations.
- Integrate deep learning models for better user preference predictions.
- Expand the database with more book details and user interactions.
This project showcases machine learning in real-world applications, demonstrating expertise in Python, data science, and web app development.