Book Recommender System

A machine learning-based system to recommend books to users based on their preferences and similar user ratings. This project uses collaborative filtering and the Nearest Neighbors algorithm for personalized recommendations.

---

Features

• Data Processing: Preprocessed datasets for books, users, and ratings.
• Book Recommendation: Suggests similar books using user ratings.
• Interactive Functionality: Easily search for a book and get recommendations.
• Model Artifacts: Includes trained models and serialized data for reuse.

---

Datasets

The system uses the following datasets:

1. Books: Contains book titles, authors, publishers, and image URLs.
2. Users: Information about users who rated books.
3. Ratings: Book ratings provided by users.

---

How It Works

1. Data Preprocessing:
   • Cleaned and structured datasets.
   • Filtered active users and books with significant ratings.
2. Model Training:
   • Built a pivot table of book ratings.
   • Trained a Nearest Neighbors model using the sparse representation of the data.
3. Recommendations:
   • Input a book name to get a list of similar books based on user preferences.

---

Example Usage

# Import the recommendation function
from recommend import recommend_book

# Provide a book name
book_name = "Harry Potter and the Chamber of Secrets (Book 2)"

# Get recommendations
recommend_book(book_name)