https://github.com/arrnikhilchauhan/book-recommendation
This Book Recommendation System is designed to provide personalized book suggestions based on user ratings and interaction. By leveraging the power of Pandas and NumPy, it filters and processes data efficiently, ensuring that recommendations are relevant and tailored to the user’s preferences
https://github.com/arrnikhilchauhan/book-recommendation
numpy pandas-library python
Last synced: 3 months ago
JSON representation
This Book Recommendation System is designed to provide personalized book suggestions based on user ratings and interaction. By leveraging the power of Pandas and NumPy, it filters and processes data efficiently, ensuring that recommendations are relevant and tailored to the user’s preferences
- Host: GitHub
- URL: https://github.com/arrnikhilchauhan/book-recommendation
- Owner: ArrNikhilchauhan
- Created: 2025-04-28T05:38:30.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2025-04-28T05:55:07.000Z (about 1 year ago)
- Last Synced: 2025-04-28T06:31:24.366Z (about 1 year ago)
- Topics: numpy, pandas-library, python
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Book Recommendation System
This is a **Book Recommendation System** designed to provide personalized book suggestions based on user ratings and interactions. It uses **Pandas** and **NumPy** for efficient data manipulation and numerical operations, ensuring fast and scalable recommendations.
## Features
### 1. Home Page
- The **Home Page** displays the top 30 books, providing users with a quick overview of the most popular choices.
### 2. Navbar Options
- **Recommend**: Users can access personalized book recommendations based on their ratings and interactions.
- **Search**: Users can search for books by title or author and find detailed information about each book.
- **Top 30**: This option allows users to view the top 30 recommended books based on ratings and user interactions.
### 3. Data Processing
- The system processes large datasets using **Pandas** for data manipulation and **NumPy** for handling numerical operations, ensuring optimal performance.
- Filters recommendations based on user ratings and interactions, providing highly relevant suggestions.
## Libraries Used
- **Pandas**: For handling and processing the book and user interaction data.
- **NumPy**: For numerical operations, particularly for efficiently working with large datasets.
- **Flask/React**: (Optional: Specify if you used a web framework for the front end).