Book Recommendation System

Description

📚 Book Recommendation System Using KNN – Machine Learning Project

This Book Recommendation System using KNN (K-Nearest Neighbors) implements item-item collaborative filtering to recommend similar books based on user rating patterns. The system calculates cosine similarity on a sparse user-book matrix to generate accurate Top-N recommendations.

🚀 Key Features

• ✔ K-Nearest Neighbors Algorithm
• ✔ Cosine Similarity Implementation
• ✔ User-Book Sparse Matrix
• ✔ Top-N Similar Book Recommendations
• ✔ Similarity Score Display
• ✔ FastAPI REST Backend
• ✔ Streamlit Interactive UI
• ✔ Professional Folder Structure
• ✔ Clean & Commented Code
• ✔ Ready-to-Run Project

💻 Tech Stack

• Python
• Pandas & NumPy
• Scikit-learn
• FastAPI
• Streamlit
• CSR Sparse Matrix

🎯 Why This Project is Special

• Real-world recommendation logic
• Industry-style architecture
• Efficient handling of sparse datasets
• Easy to explain in viva

📦 Complete Package Includes

• Full Working Source Code
• Project Report (DOCX)
• PPT for Presentation

Perfect for Final Year Major Project, Machine Learning Submission, and Academic Demonstration.