Genre-Based Book Recommendation System

This is a Streamlit web application that provides book recommendations based on genres using machine learning techniques. The app utilizes the CountVectorizer from Scikit-learn to convert genres into numerical vectors and then uses cosine similarity to recommend books that are similar to the selected book.

Features

• Book Recommendations: Select a book, and the app will recommend the top 5 books based on the genre similarity.
• Cosine Similarity: The app uses cosine similarity to find books with the closest genre matches.
• Integrated Dataset: The app comes with a pre-loaded CSV file (books.csv) containing book titles and genres.

Demo

You can see a working demo of the app below:

Book Recommendation App Demo

How it works

1. The user selects a book title from the dropdown list.
2. The app computes similarity between the selected book and all other books based on their genres.
3. It recommends the top 5 books that are most similar to the selected one.

Dataset

The app uses a CSV file named books.csv that contains the following columns:

• Title: The title of the book.
• Genre: The genre of the book.

Sample data format:

Title	Genre
Harry Potter and the Sorcerer's Stone	Fantasy
The Hobbit	Fantasy
The Da Vinci Code	Mystery
A Brief History of Time	Science

Technologies Used

• Streamlit: For the web interface.
• Pandas: To handle data loading and manipulation.
• Scikit-learn: To compute cosine similarity using CountVectorizer.

Installation

Follow these steps to set up the app locally:

1. Clone the repository:

   git clone https://github.com/your-repo/book-recommendation-app.git
   cd book-recommendation-app

2. Install the required packages: Make sure you have Python installed, then run:

   pip install -r requirements.txt

3. Run the app:

   streamlit run app.py

4. Access the app: Once the app is running, open your browser and go to http://localhost:8501/.

Code Overview

Machine Learning Algorithm

The app uses CountVectorizer to convert the 'Genre' column into a vector matrix and calculates the similarity scores using cosine similarity. The top 5 books with the highest similarity scores are then displayed as recommendations.

Key Functions

• get_index_from_title(title): Finds the index of a book by its title.
• get_title_from_index(index): Returns the title of a book from its index.
• cosine_similarity: Computes the similarity between all books based on their genres.

Files in the Repository

• app.py: The main application code.
• books.csv: The dataset with book titles and genres.
• requirements.txt: The list of required packages.
• README.md: This documentation file.

Example Output

• Selected Book: Harry Potter and the Sorcerer's Stone
• Recommended Books:
  1. The Hobbit (Score: 0.89)
  2. The Fellowship of the Ring (Score: 0.87)
  3. Eragon (Score: 0.86)
  4. The Chronicles of Narnia (Score: 0.85)
  5. The Golden Compass (Score: 0.83)

License

This project is licensed under the MIT License. See the LICENSE file for details.

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