A machine learning-powered Book Recommender System that provides personalized book recommendations to users based on their reading preferences and habits. The system uses collaborative filtering and content-based filtering techniques to suggest books that align with users' interests.
- Personalized Recommendations: Get book suggestions tailored to your reading history and preferences
- Similar Book Discovery: Find books similar to ones you've enjoyed in the past
- User-friendly Interface: Simple and intuitive design for seamless navigation
- Diverse Book Collection: Access recommendations from a vast library of books across various genres
- Real-time Processing: Quickly generate recommendations using optimized algorithms
- Python: Core programming language
- Scikit-learn: For implementing machine learning algorithms
- Pandas: For data manipulation and analysis
- NumPy: For numerical computing
- Flask: Web framework for building the application
- HTML/CSS/JavaScript: Front-end development
- Render: Hosting platform for deployment
- Clone the repository
git clone https://github.com/AdilShamim8/Book-Recommender-System.git
cd Book-Recommender-System- Create and activate a virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate- Install dependencies
pip install -r requirements.txt- Run the application
python app.py- Open your browser and navigate to
http://localhost:5000
Book-Recommender-System/
├── Datasets/ # Contains book data and user ratings
├── Model/ # ML models and preprocessing scripts
├── Website/ # Frontend implementation
│ ├── static/ # CSS, JS, and image files
│ ├── templates/ # HTML templates
│ └── app.py # Flask application
├── LICENSE # License information
└── README.md # Project documentation
The recommender system works by analyzing patterns in user ratings and book metadata. It employs two main approaches:
- Collaborative Filtering: Recommends books based on user similarity
- Content-Based Filtering: Recommends books with similar content features
The system processes user input, compares it against the trained model, and generates a list of recommended books that the user might enjoy.
The recommendation system is built using the following datasets:
- Books metadata (titles, authors, publishers, etc.)
- User ratings and reviews
- Book categories and genres
- Implement hybrid recommendation techniques
- Add user authentication and profiles
- Incorporate natural language processing for review analysis
- Enhance mobile responsiveness
- Add book availability from various online stores
This project is licensed under the MIT License - see the LICENSE file for details.
- Goodreads for the inspiration
- All the open-source libraries that made this project possible
- Dataset providers for making the book data accessible for analysis
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