https://github.com/cnoret/book_recommender
Streamlit-based application that provides personalized book recommendations using a k-Nearest Neighbors (kNN) model.
https://github.com/cnoret/book_recommender
knn python recommendation-system scraping streamlit truncated-svd
Last synced: 26 days ago
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Streamlit-based application that provides personalized book recommendations using a k-Nearest Neighbors (kNN) model.
- Host: GitHub
- URL: https://github.com/cnoret/book_recommender
- Owner: cnoret
- License: mit
- Created: 2025-01-28T14:06:21.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-02T15:27:40.000Z (over 1 year ago)
- Last Synced: 2025-07-03T09:43:20.551Z (11 months ago)
- Topics: knn, python, recommendation-system, scraping, streamlit, truncated-svd
- Language: Jupyter Notebook
- Homepage: https://book-recommender-eng.streamlit.app
- Size: 71.5 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 📚 Book Recommender System
Welcome to the **Book Recommender System**! This project is a Streamlit-based application that provides personalized book recommendations using a k-Nearest Neighbors (kNN) model, and a collaborative filtering approach via SVD.
---
## 🔍 Features
### 🔢 General Statistics
- Displays the total number of books and users in the dataset.
### 🎮 Popular Books
- Shows the most popular books based on the number of ratings.
### 🌟 Top-Rated Books
- Highlights the top-rated books in the dataset.
### 🔍 Recommendations
- Provides book recommendations based on user selection.
- Allows users to rate recommendations and save their feedback.
### 🕵️♂️ Advanced Search
- Search for books by keywords.
### 🌐 Random Book Discovery
- Discover a random book from the dataset.
### 🎨 Visualizations
- View distributions of ratings and user interactions with books.
---
## 💪 Technologies Used
- **Streamlit**: For building the web-based user interface.
- **scikit-learn**: For implementing the kNN recommendation algorithm.
- **pandas**: For data manipulation.
- **plotly**: For creating interactive visualizations.
- **pickle**: For saving and loading the pre-trained model and datasets.
- **SVD (Singular Value Decomposition)**: For collaborative filtering-based recommendations.
---
## 🚀 How to Run the Application
### Prerequisites
- Python 3.9 or later
- Required Python libraries (listed in `requirements.txt`):
### Steps to Run
1. Clone the repository:
```bash
git clone https://github.com/your-username/book-recommender-system.git
cd book-recommender-system
```
2. Install the dependencies:
```bash
pip install -r requirements.txt
```
3. Place the required data and model files in the `artifacts/` directory:
- `knn_model.pkl`
- `svd_model.pkl`
- `book_titles.pkl`
- `book_df.pkl`
4. Run the Streamlit app:
```bash
streamlit run app.py
```
5. Open your web browser and navigate to `http://localhost:8501`.
---
## 🌄 File Structure
```
book-recommender-system/
├── app.py # Main application script
├── utils.py # Functions library
├── tabs/ # Tabs functions library
│ ├── tab0.py
│ ├── tab1.py
│ ├── tab2.py
│ ├── tab3.py
│ ├── tab4.py
│ ├── tab5.py
│ └── tab6.py
├── artifacts/ # Contains model and dataset files
│ ├── svd_model.pkl # Trained SVD model
│ ├── knn_model.pkl # Trained KNN model
│ ├── book_df.pkl # Book DataFrame
│ └── book_titles.pkl # Titles of the books
├── data/ # Raw and cleaned datasets
│ ├── dataset.csv # Raw dataset with book info
│ ├── Ratings.csv # Ratings data
│ ├── dataset_with_details.csv # Extended dataset
│ ├── cleaned_data.csv # Preprocessed dataset
│ ├── Users.csv # User data
│ └── Books.csv # Book data
├── scrapper/ # Scraping scripts
│ ├── scrapper.py # Main scraping script
│ └── scrapper_cache_check.py # Script to verify cached data
├── notebooks/ # Jupyter notebooks for model training and analysis
│ ├── base.ipynb # Basic EDA notebook
├── Dockerfile # Docker configuration file
├── train.py # Script for training the recommendation model
├── requirements.txt # Dependencies for the project
├── runtime.txt # Specifies Python version for deployment
├── README.md # Project documentation
├── LICENSE # License information
└── .gitignore # Git ignore file
```
### Description of Key Files
- **artifacts/**: Contains all the generated artifacts from the training process, including the trained models and processed data.
- **data/**: Directory for raw and cleaned datasets.
- **scrapper/**: Contains the scraping scripts used to collect book and user data.
- **notebooks/**: Jupyter notebooks for exploratory data analysis (EDA) and model training.
- **Dockerfile**: Used for containerizing the application.
- **app.py**: The main application file for deploying the recommendation engine.
- **requirements.txt**: Lists all the Python dependencies required for the project.
- **runtime.txt**: Specifies the runtime environment for deployment (e.g., Python version).
- **train.py**: A standalone Python script for training the recommendation engine model.
---
## 🎨 Screenshots
### 🌐 Home Page
### 🔍 Recommendations
### 🎨 Visualizations
---
## ⚖️ License
This project is licensed under the MIT License. See the `LICENSE` file for details.
---
Thank you for exploring the Book Recommender System! 🚀