This project is a Book Recommendation System with a graphical user interface built using Tkinter. It uses collaborative filtering and TF-IDF (Term Frequency-Inverse Document Frequency) to suggest books based on user input.

• User-friendly GUI for easy interaction
• Book recommendations based on title input
• Displays book details including title, author, publication year, and publisher
• Shows book cover images for visual reference
• Ability to view larger cover images

• Python 3.7+
• pandas
• numpy
• scikit-learn
• tkinter
• pandastable
• Pillow
• requests

1. Clone this repository:

   git clone https://github.com/ayushnangia/Bookrecomd.git
   cd Bookrecomd
   

2. Install the required packages:

   pip install pandas numpy pillow requests pandastable sklearn
   

3. Download the dataset files (Books.csv, Users.csv, Ratings.csv) and place them in the project directory.

1. Run the main script:

   python project.py
   

2. Enter a book title in the input field.

3. Click the "Get Recommendations" button to see similar book recommendations.

4. Click on "View Cover" to see a larger version of the book cover.

1. The system loads and preprocesses data from CSV files containing book information, user data, and ratings.

2. It uses TF-IDF vectorization on book titles to create a matrix representation of the books.

3. Cosine similarity is computed between book vectors to find similar books.

4. When a user inputs a book title, the system finds the most similar books based on cosine similarity scores.

5. The recommendations are displayed in a table format with book details and cover images.

• project.py: The main script that runs the GUI and recommendation system.
• Books.csv: Dataset containing book information.
• Users.csv: Dataset containing user information.
• Ratings.csv: Dataset containing user ratings for books.
• images/: Directory to store downloaded book cover images.
• logo.ico: Icon file for the application window.