Book Recommendation System
About the Project
The Book Recommendation System is a free, open-source web application that helps users find book suggestions using data-driven logic. Built using Python (Flask), it implements both popularity-based and collaborative filtering methods to recommend books intelligently.
This project is ideal for:
- Students working on data science or machine learning projects
- Beginners exploring recommendation systems
- Developers creating portfolio projects
| Project Name | Language Used | Developer |
|---|---|---|
| Book Recommendation System | Python, HTML, CSS |
Project Summary
We worked with a dataset containing three core files:
Books.csvBook info (title, author, year, etc.)Users.csvUser info (location, age)Ratings.csvBook ratings by users
The recommendation system is built in four major steps:
1. Data Cleaning & Feature Engineering
- Removed missing or irrelevant values
- Extracted meaningful features for analysis
2. Popularity-Based Recommendations
- Merged book and ratings data on ISBN
- Displayed top 50 books with the highest average ratings
- Filtered books with more than 250 ratings for better reliability
3. Collaborative Filtering
- Used memory-based filtering
- Filtered active users (rated 200+ books) and popular books (rated by 50+ users)
- Applied cosine similarity for item-item matching
- Generated recommendations using similarity scores
4. Book Recommendation Function
- Accepts a book name as input
- Returns 4 similar books along with authors using a trained model
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Technology Stack
- Language: Python
- Framework: Flask
- Front-End: HTML, CSS
- Libraries: Pandas, NumPy, Scikit-learn
- Model Storage: Pickle (
.pkl) files - Notebook for Development: Jupyter
Available Features
- Shows top-rated books based on rating count and average score
- Recommends similar books based on item similarity
- Uses collaborative filtering for personalized suggestions
- Lightweight no external database required
- Includes a full development notebook and web interface
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