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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