📘 README: Book Recommendation System

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📖 Overview

This Book Recommendation System suggests similar books based on a user's selection using collaborative filtering and a trained KNN model. It enhances user experience with a visually engaging Streamlit interface, only recommending books with ratings above 3 and available cover images.

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🧠 Features

• ML-based book similarity search using K-Nearest Neighbors (KNN)
• Interactive UI built with Streamlit
• Searchable sidebar for selecting books
• Displays cover image, author, rating, and publication year
• CSS-enhanced styling for better visual presentation
• Filters recommendations by rating and image availability

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🔧 Installation

1. Clone the Repository

   git clone https://github.com/yashcahvanweb/Book_recommendation_system-Machine_Learning.git
   cd Book_recommendation_system-Machine_Learning

2. Install Dependencies

   pip install -r requirements.txt

3. Run the Application

   streamlit run app.py

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📁 Project Structure

├── artifacts/
│   ├── model.pkl
│   ├── book_name.pkl
│   ├── final_rating.pkl
│   └── book_pivot.pkl
├── styles.css
├── app.py
├── README.md
└── requirements.txt

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🧪 Requirements

• Python 3.7+
• Streamlit
• NumPy
• Pandas
• Scikit-learn
• Pickle

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📸 Sample Output

• Book cards with titles, authors, rating stars, and background images
• Clean, responsive layout for recommendations
• Sidebar for user search and selection

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🚀 Future Improvements

• Add user-based collaborative filtering
• Include genre filters or personalized profiles
• Integrate external book APIs for real-time metadata