Book Recommendation System

Overview

This project aims to develop a book recommendation system using two key approaches: Popularity-Based Recommendation and Collaborative Filtering. Additionally, the project includes Exploratory Data Analysis (EDA) to understand the underlying patterns in the book, user, and rating data. The dataset used for this project consists of books, users, and ratings information.

Features

• Popularity-Based Recommendation: Recommends books that are popular among users, either by the number of ratings or by average ratings.
• Collaborative Filtering: Provides personalized book recommendations based on user behavior and preferences.
• Exploratory Data Analysis (EDA): Analyzes and visualizes key insights from the data, including book ratings, user demographics, and rating trends.

Datasets

Books Dataset

The books dataset contains information on various books, including:

• ISBN: Unique identifier for the book.
• Book-Title: Title of the book.
• Book-Author: Author of the book.
• Year-Of-Publication: Year the book was published.
• Publisher: Publisher of the book.
• Image-URL-S, Image-URL-M, Image-URL-L: URLs for the book's images in different sizes.

Users Dataset

The users dataset includes:

• User-ID: Unique identifier for the user.
• Location: User's location (city, state, country).
• Age: Age of the user.

Ratings Dataset

The ratings dataset provides:

• User-ID: Unique identifier for the user.
• ISBN: Unique identifier for the book.
• Book-Rating: Rating given to the book (range from 0 to 10).

Installation and Setup

Clone the repository:

git clone https://github.com/yourusername/book-recommendation-system.git

To begin, install the required packages:

pip install pandas numpy seaborn matplotlib