The Book Recommender System is a machine learning-powered web app that helps users discover books based on their interests. Using collaborative filtering, it suggests books similar to the ones a user searches for.
- Browse Top 50 Books with cover images, authors, ratings, and votes.
- Enter a book name to get personalized recommendations.
- Modern dark-themed UI with hover effects and responsive hamburger menu.
- Fast, data-driven recommendations using collaborative filtering.
- Fully functional Flask backend integrated with ML models.
- Frontend: HTML, CSS, Bootstrap 3
- Backend: Python, Flask
- ML/AI: Collaborative Filtering, NumPy, Pickle
- Deployment: Render, Hugging Face Spaces, or Heroku
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Clone the repository
git clone https://github.com/bharti78/Book-Recommender-System cd book-recommender-system -
Install dependencies
pip install -r requirements.txt
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Run the app locally
python app.py
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Open your browser and go to:
http://127.0.0.1:5000
- Render: Recommended for free hosting. Use the following start command:
gunicorn app:app
- Add user login and personalized history
- Advanced recommendations using NLP & semantic search
- Dark/Light mode toggle
- Pagination for large datasets
This project demonstrates ML integration with web development, providing a clean, recruiter-friendly, fully functional book recommendation system for your portfolio.
This project is licensed under the MIT License.