📚 Book Recommendation System

A machine learning–powered Book Recommendation System with two main features:

• Popularity-Based Recommendations: Discover trending books loved by many readers.
• Collaborative Filtering: Get personalized book suggestions based on your favorite reads.

🌐 Live Demo: Book Recommender on Render

---

🚀 Features

🏠 Home Page – Popularity-Based

• Displays the Top 50 Most Popular Books.
• Ranks books based on average ratings and number of ratings.
• Helps users quickly explore trending and highly rated books.

🔍 Recommendation Page – Collaborative Filtering

• Enter the name of a book you like.
• The system recommends 5 similar books using collaborative filtering.
• Uses similarity algorithms (e.g., Cosine Similarity, KNN) to analyze user preferences.

---

⚙️ How It Works

Data Processing

• Preprocessed dataset of books, ratings, and user interactions.
• Cleaned data stored as .pkl files for faster access.

Popularity Model

• Books ranked by average rating and number of ratings.

Collaborative Filtering

• Finds similar books using similarity scores.
• Provides personalized recommendations.

User Interface

• Simple and interactive web interface.

---

🛠 Tech Stack

• Python: Pandas, NumPy, Scikit-learn
• Flask: Web app framework (served on Render)
• Pickle: To store preprocessed models/data
• HTML/CSS: For frontend styling

---

▶️ Run Locally

Clone the repository:

git clone https://github.com/ADITHICJ/Book-Recommender.git
cd Book-Recommender

Create a virtual environment and install dependencies:

python -m venv .venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate
pip install -r requirements.txt

Run the app

python app.py

Open in your browser

http://127.0.0.1:5000

---

📊 Example Screens

• Home Page: Displays top 50 trending books.
• Recommendation Page: Enter a favorite book to view personalized suggestions.

---

💡 Outcome

This project demonstrates how data-driven approaches can enhance user experience by providing recommendations similar to those on platforms like Goodreads or Amazon.

---

📚 References

• Pandas Documentation
• Scikit-learn Documentation
• Flask Documentation
• NumPy Documentation

---

Developed by ADITHICJ