This project is a book recommendation system developed using Python, HTML, Bootstrap, Pickle, NumPy, and Pandas.

The book recommendation system employs collaborative filtering to suggest books to users based on their preferences and interactions with similar users.

Collaborative filtering recommends items to users based on the preferences of similar users. It leverages patterns in user behavior to offer personalized suggestions.. For example:

Now, let's say a seventh user wants a recommendation, and they've already read Book A. We can use collaborative filtering to suggest books similar to those liked by users who also liked Book A. In this case, User 1 and User 2 have both rated Book A highly and have similar tastes to our seventh user. So, based on collaborative filtering, we might recommend books that these users also liked, such as Book B and Book E, to our seventh user.

• Home Page: Displays a list of popular and highly rated books.

• Recommendation: Users can input a book name, and the system recommends similar books based on collaborative filtering.

• Search Books: Allows users to search for specific books.

• Contact: Provides a contact form for users to send messages.

The dataset used for this project is sourced from Kaggle and includes three files: Books.csv, Users.csv, and Ratings.csv. These files contain information about books, users, and their ratings, respectively.

1. Install the required dependencies listed in requirements.txt.
2. Run app.py to start the Flask server.
3. Access the application through the provided URL.
4. Navigate through the various sections: Home, Recommend, Search Books, and Contact.