15% OFF Book Recommendation System Using KNN
The Book Recommendation System Using KNN is a Python-based machine learning project that recommends similar books using collaborative filtering and K-Nearest Neighbors. It processes book ratings, creates a user-book rating matrix, and uses similarity calculations to return relevant book recommendations.
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Project Overview
The Book Recommendation System Using KNN is a machine learning project designed to help readers discover books similar to those they already enjoy. It uses collaborative filtering based on user ratings and K-Nearest Neighbors (KNN) to identify books with similar rating patterns.
The system processes book, user, and rating datasets, cleans the data, creates a user-book rating matrix, and uses similarity calculations to generate recommendations. The project uses Python and machine learning libraries to build the recommendation workflow.
Available Features
- Dataset Loading: Load book, user, and rating datasets.
- Data Cleaning: Remove duplicate records and handle missing values.
- User-Book Rating Matrix: Create a matrix representing users and their book ratings.
- KNN Model: Find similar books using K-Nearest Neighbors.
- Collaborative Filtering: Generate recommendations based on user rating patterns.
- Book Search: Search for a book by title.
- Similar Book Recommendations: Return recommended books based on similarity.
- Similarity Scores: Optionally display similarity scores for recommendations.
- Sparse Matrix Processing: Handle rating data efficiently when most users have not rated most books.
Which Technologies Are Used in This Project?
- Python: Main programming language.
- Pandas: Data cleaning and manipulation.
- NumPy: Numerical operations.
- Scikit-learn: KNN and similarity-based machine learning.
- SciPy: Sparse matrix processing.
- Streamlit or HTML/CSS/JavaScript: Possible interface options described in the article.
- Flask or FastAPI: Optional backend frameworks.
- SQLite or PostgreSQL: Optional database technologies.
More Details and Full Documentation
Visit the official UPDATEGADH project page for the complete project description, system design, screenshots, and source package information.
Book Recommendation System Using KNN – UPDATEGADH
Watch Project Demo
Watch the Book Recommendation System Using KNN project demo on YouTube
- Book, User, and Rating Dataset Loading
- Data Cleaning and Preprocessing
- User-Book Rating Matrix Creation
- Sparse Matrix Processing
- KNN Model Training
- Similarity Calculation
- Book Title Search
- Similar Book Recommendation
- Similarity Score Display (Optional)
Step 1: Install Python and Visual Studio Code.
Step 2: Download and extract the project source package.
Step 3: Open the extracted project folder in VS Code.
Step 4: Open Terminal > New Terminal.
Step 5: Create a virtual environment
python -m venv venv
Step 6: Activate the environment on Windows
venv\Scripts\activate
Step 7: Install the dependencies.
If requirements.txt is provided
pip install -r requirements.txt
Otherwise, the libraries may include
pip install pandas numpy scikit-learn scipy
Step 8: Check the project files for the book, user, and rating datasets.
Step 9: Locate the main application file and follow the framework specified in the source code.
For a Streamlit application
streamlit run app.py
Replace app.py with the actual filename if different.
Step 10: Open the local address displayed in the terminal.
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