An interactive machine learning app that suggests similar books based on your favorite titles. Built with Pandas, scikit-learn, and Streamlit, this project helps users discover new reads by leveraging content-based filtering techniques.
- 🔎 Content-Based Recommendations: Suggests books with similar themes, genres, and descriptions.
- 📊 Clean UI with Streamlit: Intuitive interface for selecting and recommending books.
- 💾 Preloaded Dataset: Includes thousands of book titles, authors, and descriptions.
- 🌐 Deployable Web App: Fully functional and deployable on platforms like Streamlit Cloud or Hugging Face Spaces.
The landing page of the app allows users to select any book from the dropdown and get personalized recommendations.
After selecting "Mestizos Come Home!", the app lists multiple similar books that the user might enjoy.
A clean, vertical layout showcasing additional recommendations along with book covers and titles.
Selecting a different title like "Philosophy of david cronenberg" generates new, relevant book suggestions.
- Python
- Pandas
- scikit-learn
- Streamlit
- Book dataset (custom CSV)
The model uses cosine similarity on TF-IDF vectors generated from book titles and metadata. When a user selects a book, the system finds other books with the closest feature vectors, which are then displayed as recommendations.
- Clone the repository
git clone https://github.com/your-username/book-recommender.git
cd book-recommender