Project Name: Book Recommendation System

Project Description:

This project implements two types of book recommendation systems: popularity-based and collaborative filtering-based. The popularity-based recommender suggests popular books based on the number of ratings and average ratings received from users. The collaborative filtering-based recommender recommends books similar to the one provided as input, based on user-item interactions.

Table of Contents:

• Project Demo
• Badges
• Installation
• Usage
• Configuration
• Contributing
• License
• Acknowledgments
• Documentation

Project Demo:

The project demo is not provided here, but you can run the code in your local environment to see how the book recommendation systems work for different scenarios.

Badges:

Installation:

1. Make sure you have Python 3.7 or later installed.
2. Clone this repository to your local machine.
3. Install the required dependencies by running the following command in your terminal:

   pip install numpy pandas scikit-learn

Usage:

1. Ensure you have the required dataset files "Books.csv", "Users.csv", and "Ratings.csv" in the project directory.
2. Run the provided Python code to load and preprocess the dataset.
3. The code contains two types of book recommendation systems:
   • Popularity-Based Recommender: It suggests popular books based on the number of ratings and average ratings.
   • Collaborative Filtering-Based Recommender: It recommends books similar to the one provided as input, based on user-item interactions.
4. You can call the recommend() function with the name of the book you want recommendations for in the collaborative filtering-based recommender.

Configuration:

There is no specific configuration required for this project. However, you can experiment with different datasets or fine-tune the collaborative filtering algorithm for better recommendations.

Contributing:

Contributions to this project are welcome. If you find any bugs or want to add new features, please follow these steps:

1. Fork the repository.
2. Create a new branch with a descriptive name for your feature or bug fix.
3. Make your changes and test thoroughly.
4. Create a pull request to merge your changes into the main branch.

License:

This project is licensed under the MIT License.

Acknowledgments:

This project uses several external libraries and tools such as NumPy, Pandas, and scikit-learn. Credits and thanks go to the respective developers and communities for their valuable contributions.

Documentation:

For more detailed documentation and explanation of the code, you can visit the GitHub repository of this project.