https://github.com/sshbuilder/amazon-book-recommendation-system
This is an end to end book recommendation system project | Part of my effort to showcase the lenght and breath of my skills
https://github.com/sshbuilder/amazon-book-recommendation-system
Last synced: 3 months ago
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This is an end to end book recommendation system project | Part of my effort to showcase the lenght and breath of my skills
- Host: GitHub
- URL: https://github.com/sshbuilder/amazon-book-recommendation-system
- Owner: sshBuilder
- Created: 2024-08-20T12:14:16.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-08-26T08:23:28.000Z (9 months ago)
- Last Synced: 2025-02-26T19:48:51.744Z (3 months ago)
- Language: Jupyter Notebook
- Size: 536 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Amazon Book Recommendation System
## Overview
This project aims to build an end-to-end book recommendation system using the [Amazon Books Dataset](https://www.kaggle.com/datasets/chhavidhankhar11/amazon-books-dataset). The system will recommend books to users based on their preferences and behavior, leveraging advanced recommendation algorithms.
## Dataset
The dataset used in this project is sourced from Kaggle and contains detailed information about Amazon book reviews. The dataset includes:
- Book titles
- Authors
- Average ratings
- Review counts
- Categories
- Additional metadata
## Objectives
- **Data Exploration**: Analyze and preprocess the dataset to prepare it for modeling.
- **Model Selection**: Implement and compare various recommendation algorithms including collaborative filtering, content-based filtering, and hybrid approaches.
- **Model Training**: Train the recommendation models on the dataset.
- **Evaluation**: Assess the performance of the models using metrics such as RMSE, precision, and recall.
- **Deployment**: Develop a user interface or API for users to interact with the recommendation system.
## Project Structure
The project directory is organized as follows:
```
/amazon-book-recommendation-system
├── data/
│ └── amazon_books.csv # The dataset file
├── notebooks/
│ └── data_exploration.ipynb # Jupyter Notebook for data exploration
├── src/
│ ├── data_preprocessing.py # Data preprocessing scripts
│ ├── recommendation_models.py # Recommendation algorithms
│ └── evaluation.py # Model evaluation scripts
├── requirements.txt # Project dependencies
├── app.py # Flask/Django application file (if applicable)
├── README.md # This file
└── results/
└── evaluation_results.csv # Model evaluation results
```
## Installation
To set up the project, follow these steps:
1. **Clone the repository**:
```bash
git clone https://github.com/yourusername/amazon-book-recommendation-system.git
cd amazon-book-recommendation-system
```
2. **Create and activate a virtual environment**:
```bash
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
```
3. **Install the dependencies**:
```bash
pip install -r requirements.txt
```
4. **Download the dataset** from [Kaggle](https://www.kaggle.com/datasets/chhavidhankhar11/amazon-books-dataset) and place it in the `data/` directory.
## Usage
1. **Data Exploration**:[README.md](README.md)
Run the[README.md](README.md) Jupyter Notebook in `notebooks/data_exploration.ipynb` to explore and preprocess the data.
2. **Model Training**:
Execute the scripts in `src/recommendation_models.py` to train the recommendation models.
3. **Evaluation**:
Use `src/evaluation.py` to evaluate the performance of the models.
4. **Run the Application** (if applicable):
```bash
python app.py
```
## Contributing
Feel free to contribute to the project by submitting issues or pull requests. For major changes, please open an issue first to discuss what you would like to change.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Acknowledgements
- The dataset is provided by [Chhavi Dhankhar](https://www.kaggle.com/datasets/chhavidhankhar11/amazon-books-dataset).
- Special thanks to contributors and libraries used in this project.
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