Such-13/Book-Recommendation-System

Vervebridge-Book-Recommendation-System

Book Recommender System

https://vervebridge-book-recommendation-system-1.onrender.com

Overview

The Book Recommender System (https://vervebridge-book-recommendation-system-1.onrender.com) is a web application that allows users to input a book title and receive a list of recommended books based on cosine similarity of combined textual features. The system is built using Flask for the backend and leverages machine learning for generating recommendations.

System Architecture

The system follows a client-server architecture with the following major components:

• User Interface: A web interface for entering book titles and displaying recommendations.
• Backend Server: A Flask application that handles user requests and interacts with the recommendation engine.
• Recommendation Engine: A module that preprocesses data, trains the machine learning model, and provides book recommendations.
• Data Storage: Stores book data and pre-trained model files.

Technology Stack

• Frontend: HTML, CSS (for styling)
• Backend: Python (Flask framework)
• Machine Learning: Scikit-learn (for cosine similarity), Pandas (for data handling)
• Storage: CSV (for book data), Joblib (for model storage)

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/book-recommender-system.git
   cd book-recommender-system

2. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Download the dataset and model:

   • Ensure books.csv is in the project directory.
   • Ensure model.pkl (the pre-trained model) is in the project directory.

Project Structure

book-recommender-system/
├── templates/
│ └── index.html
├── static/
│ └── style.css
├── app.py
├── preprocess.py
├── recommend.py
├── train_model.py
├── books.csv
├── model.pkl
├── requirements.txt
├── logging_config.py
├── app.log
└── README.md

Execution

1. Train the Model

If you need to retrain the model or preprocess the data, run the model.py script:

python model.py

This script will preprocess the data, train the model, and save the cosine similarity matrix to model.pkl.

2. Run the Flask Application

Start the Flask server by running the following command:

python app.py

The application will be available at http://127.0.0.1:5000/.

3. Use the Application

Open a web browser and navigate to http://127.0.0.1:5000/. Enter a book title in the input form and click "Get Recommendations". The system will display a list of recommended books along with the response time.

Measuring Response Time

The application measures and displays the response time for generating recommendations. This is useful for performance analysis and optimization.

Contributing

Fork the repository.

Create a new branch (git checkout -b feature-branch).

Commit your changes (git commit -am 'Add new feature').

Push to the branch (git push origin feature-branch).

Create a new Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments: Credits the libraries and frameworks used in the project.