Book Recommendation System

Book Recommendation System

Overview

The Book Recommendation System is a machine learning application that provides personalized book recommendations using Collaborative Filtering and the Nearest Neighbors algorithm. Developed with Python and Streamlit, the application offers an intuitive interface for discovering books based on user preferences and similarity analysis.

The project is deployed on Streamlit Community Cloud, allowing users to explore recommendations directly from their web browser without any local installation.

Project Objectives

• Provide personalized book recommendations based on user preferences.
• Develop an intuitive web application for exploring recommended books.
• Deploy the application for public access and demonstration.

Key Features

• User-Based Collaborative Filtering (UBCF).
• Item-Based Collaborative Filtering (IBCF).
• Nearest Neighbors algorithm using Cosine Similarity.
• Personalized recommendation generation.
• Interactive book search functionality.
• Web-based interface built with Streamlit.

Workflow

1. Collect and preprocess user-book interaction data.
2. Create a user-item interaction matrix.
3. Compute user and item similarities using Nearest Neighbors.
4. Generate personalized book recommendations.
5. Display recommendations through an interactive Streamlit application.

Technology Stack

• Python
• Scikit-learn
• Pandas
• NumPy
• Streamlit
• Streamlit Community Cloud
• GitHub

Results & Benefits

• Generates personalized book recommendations.
• Improves book discovery using collaborative filtering.
• Provides a fast and interactive user experience.
• Accessible online through Streamlit deployment.

Project Links

Web Application:
Streamlit Deployment

GitHub Repository:
GitHub Repository