UpdateGadh
Book Recommendation System Using KNN
Build a Book Recommendation System Using KNN algorithm with Python. Complete source code, dataset & documentation for BCA/MCA students.
Book Recommendation System Using KNN
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01. Project Overview
The Book Recommendation System uses Python and the K-Nearest Neighbors (KNN) algorithm to recommend similar books based on user ratings and reading preferences. It uses collaborative filtering and similarity search to generate relevant book suggestions.
02. Key Features
- Book, User, and Rating Dataset Loading
- Data Cleaning and Missing Value Handling
- User-Book Rating Matrix Creation
- KNN-Based Similarity Search
- Book Title Search
- Top Recommended Books Display
- Similarity Score Display
- Sparse Matrix Processing
- Cold-Start Handling with Fallback Recommendations
- Optional Hybrid Recommendation System
- Optional Genre, Language, and Year Filters
- Optional Admin Panel and API Integration
03. Technology Stack
- Language: Python
- ML Libraries: Pandas, NumPy, Scikit-learn
- Matrix Processing: SciPy
- Frontend: Streamlit or HTML, CSS, and JavaScript
- Optional Backend: Flask or FastAPI
- Optional Database: SQLite or PostgreSQL
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DOWNLOAD PROJECT
https://updategadh.com/book-recommendation-system-using-knn/
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UPDATEGADH | Technology Tutorials & Projects
#BookRecommendationSystem #KNN #MachineLearning #Python #CollaborativeFiltering #DataScience #PythonProjects #UPDATEGADH
━━━━━━━━━━━━━━━━━━━━━━
01. Project Overview
The Book Recommendation System uses Python and the K-Nearest Neighbors (KNN) algorithm to recommend similar books based on user ratings and reading preferences. It uses collaborative filtering and similarity search to generate relevant book suggestions.
02. Key Features
- Book, User, and Rating Dataset Loading
- Data Cleaning and Missing Value Handling
- User-Book Rating Matrix Creation
- KNN-Based Similarity Search
- Book Title Search
- Top Recommended Books Display
- Similarity Score Display
- Sparse Matrix Processing
- Cold-Start Handling with Fallback Recommendations
- Optional Hybrid Recommendation System
- Optional Genre, Language, and Year Filters
- Optional Admin Panel and API Integration
03. Technology Stack
- Language: Python
- ML Libraries: Pandas, NumPy, Scikit-learn
- Matrix Processing: SciPy
- Frontend: Streamlit or HTML, CSS, and JavaScript
- Optional Backend: Flask or FastAPI
- Optional Database: SQLite or PostgreSQL
━━━━━━━━━━━━━━━━━━━━━━
DOWNLOAD PROJECT
https://updategadh.com/book-recommendation-system-using-knn/
━━━━━━━━━━━━━━━━━━━━━━
UPDATEGADH | Technology Tutorials & Projects
#BookRecommendationSystem #KNN #MachineLearning #Python #CollaborativeFiltering #DataScience #PythonProjects #UPDATEGADH