📘 Book Recommender App

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🌟 Overview

The Book Recommender App suggests books similar to your interests based on user ratings and book metadata. Leveraging collaborative filtering and machine learning, it provides high-quality recommendations in real time through an elegant web interface. It is an intelligent and personalized book recommendation system that helps you discover your next favorite read. Built using collaborative filtering techniques and deployed with a modern, interactive Streamlit interface.

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✅ Features

• 🔍 AI-based Book Recommendations
  Powered by a K-Nearest Neighbors (KNN) model trained on real-world user ratings.

• 🎨 Interactive UI with Streamlit
  Clean, responsive, and visually appealing front-end for easy interaction.

• 📊 Live Model Training
  Ability to retrain the model with updated data from the UI.

• 🐳 Dockerized Setup
  Fully containerized for consistent deployment and scalability.

• 📁 DVC Integration
  For version-controlled dataset and model tracking (compatible with DagsHub).

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Directory structure:
└── arpitkadam-book-recommender-app/
    ├── README.md
    ├── __init__.py
    ├── app.py
    ├── demo.py
    ├── Dockerfile
    ├── dvc.yaml
    ├── LICENSE
    ├── main.py
    ├── requirements.txt
    ├── setup.py
    ├── template.py
    ├── .dockerignore
    ├── .dvcignore
    ├── artifacts/
    │   ├── data_ingestion/
    │   │   ├── ingested_data/
    │   │   │   ├── BX-Book-Ratings.csv
    │   │   │   ├── BX-Books.csv
    │   │   │   └── BX-Users.csv
    │   │   └── raw_data/
    │   │       └── archive.zip
    │   ├── data_transformation/
    │   │   └── transformed_data/
    │   │       ├── book_names.pkl
    │   │       ├── book_pivot.pkl
    │   │       ├── final_ratings.pkl
    │   │       └── transformed_data.pkl
    │   ├── data_validation/
    │   │   ├── clean_data/
    │   │   │   └── final_ratings.csv
    │   │   └── serialized_data/
    │   │       └── final_ratings.pkl
    │   └── model_trainer/
    │       └── model.pkl
    ├── config/
    │   └── config.yaml
    ├── research/
    │   ├── archive.zip
    │   ├── BX-Book-Ratings.csv
    │   ├── BX-Books.csv
    │   ├── BX-Users.csv
    │   ├── notebook.ipynb
    │   └── artifacts/
    │       ├── book_names.pkl
    │       ├── book_pivot.pkl
    │       ├── final_ratings.pkl
    │       └── model.pkl
    ├── src/
    │   ├── __init__.py
    │   ├── components/
    │   │   ├── __init__.py
    │   │   ├── data_ingestion.py
    │   │   ├── data_transformation.py
    │   │   ├── data_validation.py
    │   │   └── model_trainer.py
    │   ├── configuration/
    │   │   ├── __init__.py
    │   │   └── configuration.py
    │   ├── constants/
    │   │   └── __init__.py
    │   ├── entity/
    │   │   ├── __init__.py
    │   │   ├── artifact_entity.py
    │   │   └── config_entity.py
    │   ├── exception/
    │   │   └── __init__.py
    │   ├── logger/
    │   │   └── __init__.py
    │   ├── pipeline/
    │   │   ├── __init__.py
    │   │   ├── recommendation_pipeline.py
    │   │   └── training_pipeline.py
    │   └── utils/
    │       └── __init__.py
    └── .dvc/
        ├── config
        └── .gitignore

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⚙️ Technologies Used

• 🐍 Python 3.10+
• 📚 Pandas, NumPy, Scikit-learn
• 📈 Streamlit
• 🧠 Machine Learning (KNN-based Collaborative Filtering)
• 📦 Docker & DVC
• ☁️ DagsHub (for remote data storage)

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🐳 Docker Setup

1. Build the Docker image

docker build -t arpitkadam/book-recommender-app .

2. Run the container locally

docker run -p 8501:8501 arpitkadam/book-recommender-app

The app will be accessible at: 📍 http://localhost:8501

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📦 Installation & Setup (For Local Use Without Docker)

1. Clone the repository

git clone https://github.com/ArpitKadam/Book-Recommender-App.git
cd Book-Recommender-App

2. Create and activate a virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Launch the Streamlit app

streamlit run main.py

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🚀 How to Use

• Go to the Recommend page.
• Select a book you’ve read or liked from the dropdown.
• Click on Recommend Books to get AI-powered suggestions.
• (Optional) Use the Train Model button to retrain the model on latest data.

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🤝 Contributions

We welcome contributions! Here's how you can help:

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Submit a pull request

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📄 License

This project is licensed under the GNU General Public License v3.0.

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📬 Contact

• Email: [email protected]
• GitHub: ArpitKadam
• Personal: ArpitKadam