Working app link: https://book-recommendation-using-clustering-algorithm-uhwcaappgyxqbbu.streamlit.app/

🧠 How It Works

1. Data is collected from the Book-Crossing Dataset
2. A pivot table is created with books as rows and users as columns
3. KNN algorithm finds the most similar books based on user ratings
4. Streamlit displays the recommendations with book cover images

📊 Dataset

• BX-Books.csv — Book details (title, author, image URL)
• BX-Book-Ratings.csv — User ratings for books
• BX-Users.csv — User information

🚀 How to Run

1. Clone the repository

git clone https://github.com/Priyesh-DS-Code/Book-Recommendation-using-clustering-algorithm.git
cd Book-Recommendation-using-clustering-algorithm

2. Create virtual environment

conda create --prefix ./env python=3.11 -y
conda activate ./env

3. Install dependencies

pip install -r requirements.txt

4. Run the app

streamlit run app.py

🛠️ Tech Stack

• Python
• Pandas — Data manipulation
• NumPy — Numerical operations
• Scikit-learn — KNN Model
• Streamlit — Web application
• Pickle — Model serialization

📦 Requirements