https://github.com/subhadipsinha722133/book-recommender-system
🤖 Book Recommender System is a machine learning application designed to suggest books to users based on their preferences, reading history, and other relevant data.
https://github.com/subhadipsinha722133/book-recommender-system
clustering data-visualization machine-learning recommendation-system sklearn
Last synced: 14 days ago
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🤖 Book Recommender System is a machine learning application designed to suggest books to users based on their preferences, reading history, and other relevant data.
- Host: GitHub
- URL: https://github.com/subhadipsinha722133/book-recommender-system
- Owner: subhadipsinha722133
- Created: 2025-07-24T11:22:15.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-09-19T18:20:57.000Z (about 1 month ago)
- Last Synced: 2025-09-19T18:45:23.626Z (about 1 month ago)
- Topics: clustering, data-visualization, machine-learning, recommendation-system, sklearn
- Language: Jupyter Notebook
- Homepage: https://book-recommender-system-3jmjygcbcv6he9yovvmgxw.streamlit.app/
- Size: 13.3 MB
- Stars: 4
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 📚 Book Recommendation System
A beautiful and interactive book recommendation system built with Streamlit that suggests books based on collaborative filtering! 🌟
## ✨ Features
- 🏠 **Popular Books Dashboard** - Discover trending books with ratings and reviews
- 🔍 **Smart Recommendations** - Get personalized book suggestions
- 📊 **Visual Interface** - Beautiful book covers and organized layout
- ⚡ **Real-time Results** - Instant recommendations with just one click
- 📱 **Responsive Design** - Works perfectly on desktop and mobile
## 📺 Live Demo
🔗 [Demo Link](https://book-recommender-system-3jmjygcbcv6he9yovvmgxw.streamlit.app/)
- 🎬 Demo Video
## 🚀 Quick Start
### Prerequisites
- Python 3.8+
- pip package manager
### Installation
1. **Clone the repository**
```bash
git clone https://github.com/subhadipsinha722133/Book-Recommender-System.git
cd book-recommendation-system
```
2. **Create virtual environment** (optional but recommended)
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. **Install dependencies**
```bash
pip install -r requirements.txt
```
4. **Place your data files** in the project directory:
- `popular.pkl`
- `pt.pkl`
- `books.pkl`
- `similarity_scores.pkl`
5. **Run the application**
```bash
streamlit run main_app.py
```
6. **Open your browser** and go to `http://localhost:8501`
## 🎯 How to Use
1. **Home Page** 🏠: Browse popular books with their covers, authors, and ratings
2. **Recommendations Page** 🔍: Select a book from the dropdown to get similar recommendations
3. **Explore** 🔎: Click through different books and discover new reading options!
## 🛠️ Technologies Used
- **Frontend**: Streamlit 🎈
- **Backend**: Python 🐍
- **Data Processing**: Pandas, NumPy 📊
- **Machine Learning**: Scikit-learn 🤖
- **Image Handling**: Pillow 🖼️
## 📁 Project Structure
```
book-recommendation-system/
├── main_app.py # 🎯 Main Streamlit application
├── requirements.txt # 📦 Python dependencies
├── README.md # 📖 This file
├── popular.pkl # 📊 Popular books data
├── pt.pkl # 🔢 Pivot table data
├── books.pkl # 📚 Books metadata
└── similarity_scores.pkl # 💫 Precomputed similarity scores
```
## 🎨 Customization
Want to customize this app? Here's what you can modify:
- **Colors & Theme**: Edit Streamlit configuration in `app.py`
- **Layout**: Adjust the column structure in the UI functions
- **Data**: Replace the pickle files with your own dataset
- **Styling**: Add custom CSS through Streamlit's markdown features
## 🤝 Contributing
Want to contribute? Awesome! 🎉
1. Fork the project
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## 📧 Contact
**Subhadip Sinha** 👨💻
- GitHub: [@subhadipsinha722133](https://github.com/subhadipsinha722133)
- Email: sinhasubhadip34@gmail.com
## 🙏 Acknowledgments
- Book data sources and datasets
- Streamlit community for amazing documentation
- Open-source contributors
---
⭐ **If you like this project, give it a star on GitHub!** ⭐
Made with ❤️ by Subhadip Sinha
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