Book Recommendation System 📚

This project is a Book Recommendation System developed using Machine Learning algorithms and a Flask web interface. The system provides book recommendations based on two approaches:

1. Average Rating-Based Recommendations: Recommends books based on the average user ratings.
2. Cosine Similarity-Based Recommendations: Transforms books into vectors using user ratings and finds similar books using cosine similarity.

The dataset used for this project is sourced from Kaggle.

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Features 🚀

• Rating-Based Recommendation: Uses average user ratings to suggest popular books.
• Cosine Similarity Recommendation: Suggests books based on similarity in user preferences.
• Flask Web Interface: A simple and intuitive web application to interact with the recommendation system.

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Demo 🎥

Once the project is running, you can access the web application at http://localhost:5000 on your browser.

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Installation 💻

Follow these steps to set up and run the project on your local machine:

Prerequisites

1. Ensure you have Python 3.8+ installed.
2. Install a virtual environment tool such as venv or virtualenv.

Steps

1. Clone the repository:

   git clone https://github.com/alokranjan609/Book-recommendation-system
   cd Book-recommendation-system

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate    # On Windows, use venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Run the application:

   python app.py

5. Open a web browser and navigate to:

   http://localhost:5000
   

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Usage 🛠️

1. Enter your preferences in the search box (e.g., favorite books or genres).
2. Get recommendations based on the two implemented algorithms:
   • Top-rated books based on average ratings.
   • Similar books calculated using cosine similarity.

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Dataset 📊

The dataset used for this project is publicly available on Kaggle. You can explore the dataset here.

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File Structure 📁

• app.py: Main Flask application.
• templates/: HTML files for the web interface.
• requirements.txt: Python dependencies for the project.
• README.md: Documentation for the project.

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Dependencies 🛠️

Install all required dependencies from requirements.txt using:

pip install -r requirements.txt

Future Enhancements 🌟

• Add user authentication for personalized recommendations.
• Implement real-time feedback for improving recommendations.
• Introduce additional recommendation algorithms such as collaborative filtering or deep learning-based models.

Contribution 🤝

Contributions are welcome! Please fork the repository and submit a pull request for review.