https://github.com/md-emon-hasan/ml-project-book-recommendation-system
📚 Book recommendation system that utilizes user-friendly collaborative filtering techniques to suggest personalized book recommendations.
https://github.com/md-emon-hasan/ml-project-book-recommendation-system
artificial-intelligence book-recomendation collaborative-filtering data-science machine-learning recommendation-system recommender-system
Last synced: 4 months ago
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📚 Book recommendation system that utilizes user-friendly collaborative filtering techniques to suggest personalized book recommendations.
- Host: GitHub
- URL: https://github.com/md-emon-hasan/ml-project-book-recommendation-system
- Owner: Md-Emon-Hasan
- License: mit
- Created: 2024-08-22T18:09:32.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2024-08-23T11:08:22.000Z (about 1 year ago)
- Last Synced: 2025-01-13T08:46:20.315Z (9 months ago)
- Topics: artificial-intelligence, book-recomendation, collaborative-filtering, data-science, machine-learning, recommendation-system, recommender-system
- Language: Jupyter Notebook
- Homepage: https://ml-project-book-recommendation-system.onrender.com/
- Size: 31.2 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# 📚 Book Recommendation System
Welcome to the **Book Recommendation System** repository! This project utilizes machine learning techniques to recommend books based on user preferences and behavior.
## 📋 Contents
- [Introduction](#introduction)
- [Topics Covered](#topics-covered)
- [Getting Started](#getting-started)
- [Live Demo](#live-demo)
- [Best Practices](#best-practices)
- [FAQ](#faq)
- [Troubleshooting](#troubleshooting)
- [Contributing](#contributing)
- [Additional Resources](#additional-resources)
- [Challenges Faced](#challenges-faced)
- [Lessons Learned](#lessons-learned)
- [Why I Created This Repository](#why-i-created-this-repository)
- [License](#license)
- [Contact](#contact)
---
## 📖 Introduction
This repository showcases a machine learning project aimed at recommending books to users based on their reading preferences, ratings, and behaviors. The recommendation system helps users discover books they might enjoy by analyzing past interactions.
---
## 🔍 Topics Covered
- **Collaborative Filtering:** Implementing collaborative filtering models for book recommendation.
- **Content-Based Filtering:** Recommending books based on content similarity.
- **Data Preprocessing:** Techniques for preparing book data for modeling.
- **Model Evaluation:** Assessing recommendation system performance using precision, recall, and other metrics.
- **Deployment:** Implementing the recommendation system using Flask for a web-based interface.
---
## 🚀 Getting Started
To get started with this project, follow these steps:
1. **Clone the repository:**
```bash
git clone https://github.com/Md-Emon-Hasan/ML-Project-Book-Recommendation-System.git
```
2. **Navigate to the project directory:**
```bash
cd ML-Project-Book-Recommendation-System
```
3. **Create a virtual environment and activate it:**
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
4. **Install the dependencies:**
```bash
pip install -r requirements.txt
```
5. **Run the application:**
```bash
python app.py
```
6. **Open your browser and visit:**
```
http://127.0.0.1:5000/
```
---
## 🎉 Live Demo
Check out the live version of the Book Recommendation System app [here](https://ml-project-book-recommendation-system.onrender.com/).
---
## 🌟 Best Practices
Recommendations for maintaining and improving this project:
- **Model Updating:** Continuously update the recommendation model with new user data to improve recommendation quality.
- **Error Handling:** Implement robust error handling for user inputs and system issues.
- **Security:** Use HTTPS and proper validation for secure deployments.
- **Documentation:** Keep documentation up-to-date to support future improvements and user understanding.
---
## ❓ FAQ
**Q: What is the purpose of this project?**
A: This project recommends books to users based on their preferences and past interactions, helping them discover new books.
**Q: How can I contribute to this repository?**
A: Refer to the [Contributing](#contributing) section for details on how to contribute.
**Q: Where can I learn more about recommendation systems?**
A: Check out [Surprise Documentation](https://surprise.readthedocs.io/en/stable/) and [Kaggle](https://www.kaggle.com/learn/overview) for more information.
**Q: Can I deploy this app on cloud platforms?**
A: Yes, you can deploy the Flask app on platforms such as Heroku, Render, or AWS.
---
## 🛠️ Troubleshooting
Common issues and solutions:
- **Issue: Flask App Not Starting**
*Solution:* Ensure all dependencies are installed and the virtual environment is activated properly.
- **Issue: Model Not Loading**
*Solution:* Check the path to the model file and verify it's not corrupted.
- **Issue: Inaccurate Recommendations**
*Solution:* Verify the input features are correctly formatted and ensure the model is well-trained.
---
## 🤝 Contributing
Contributions are welcome! Here's how you can contribute:
1. **Fork the repository.**
2. **Create a new branch:**
```bash
git checkout -b feature/new-feature
```
3. **Make your changes:**
- Add features, fix bugs, or improve documentation.
4. **Commit your changes:**
```bash
git commit -am 'Add a new feature or update'
```
5. **Push to the branch:**
```bash
git push origin feature/new-feature
```
6. **Submit a pull request.**
---
## 📚 Additional Resources
Explore these resources for more insights into recommendation systems and Flask development:
- **Flask Official Documentation:** [flask.palletsprojects.com](https://flask.palletsprojects.com/)
- **Machine Learning Tutorials:** [Kaggle](https://www.kaggle.com/learn/overview)
- **Recommendation System Resources:** [Surprise](https://surprise.readthedocs.io/en/stable/)
---
## 💪 Challenges Faced
Some challenges during development:
- Handling sparse book data and user interaction features.
- Ensuring high-quality recommendations and model evaluation.
- Deploying the application and managing dependencies effectively.
---
## 📚 Lessons Learned
Key takeaways from this project:
- Practical application of recommendation systems using collaborative filtering and content-based filtering.
- Importance of thorough data preprocessing and feature selection.
- Considerations for deploying and maintaining web applications.
---
## 🌟 Why I Created This Repository
This repository was created to showcase the use of machine learning for recommending books, demonstrating the end-to-end process from data preparation to deployment.
---
## 📝 License
This repository is licensed under the [MIT License](https://opensource.org/licenses/MIT). See the [LICENSE](LICENSE) file for more details.
---
## 📬 Contact
- **Email:** [iconicemon01@gmail.com](mailto:iconicemon01@gmail.com)
- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)
- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)
- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)
---
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