📚 Book Recommender System

A personalized Book Recommender Web App built using Streamlit and powered by a collaborative filtering model. It recommends books based on what similar users have liked, and also displays the top 50 most popular books based on average rating and vote count.

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✅ Features

• Recommends 5 similar books based on user selection
• Displays top 50 most popular books (by average rating and number of votes)
• Integrated book cover images for better UX
• Clean and responsive layout with navigation buttons
• Built using Streamlit and deployable on Streamlit Cloud

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🧠 How It Works

• Collaborative Filtering is used to recommend books similar to the one selected by the user.
• The app uses cosine similarity on a pivot table of users × books (from ratings).
• Data is processed and saved in .pkl files using pickle for faster loading in the app.

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🗂️ Project Structure

book-recommender/
├── app.py → Streamlit app
├── app.png → Screenshot for README
├── books.csv → Book metadata
├── ratings.csv → User ratings
├── users.csv → User info
├── pt.pkl → Pivot table
├── books.pkl → Books metadata
├── popular_df.pkl → Top books dataframe
├── similarity_scores.pkl → Similarity matrix
├── requirements.txt → Required packages
└── README.md → Project documentation

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🧪 Tech Stack

• Python
• Streamlit (Frontend & Backend UI)
• Pandas, NumPy (Data Processing)
• Pickle (Model/Data Persistence)

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🛠️ Setup Instructions (Run Locally)

1. Clone the repository
   git clone https://github.com/yourusername/book-recommender.git cd book-recommender

2. Install the dependencies
   pip install -r requirements.txt

3. Run the Streamlit app

📌 Ensure all .pkl and dataset .csv files are in the same folder as app.py.

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📸 App Screenshot

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📞 Contact

• 📧 Email: gufrankhankab123@gmail.com
• 📱 Phone: +91-8210783123

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