Book Recommendation System is a machine learning project that suggests books to users based on Popularity-Based Filtering and Content-Based Filtering.
It allows users to discover trending books and also find books similar to the ones they love — making reading choices smarter and more personalized.

This project uses the Books Dataset from Kaggle:
📌 Kaggle Dataset - Book Recommendation Dataset

It includes:

• Users (user IDs, demographics)
• Books (title, author, publisher, etc.)
• Ratings (explicit/implicit feedback)

Watch the demo of Book Recommendation System in action:

• ⭐ Popularity-Based Recommendations
  → Suggests top-rated and trending books based on user ratings.

• 📖 Content-Based Recommendations
  → Recommends books similar to a chosen one using author, genre, and metadata.

• 🧠 Hybrid Approach
  → Combines popularity and content similarity for better results.

• 🌐 Interactive Web App
  → Simple and user-friendly interface built with Flask.

1. Popularity-Based Filtering:

   • Aggregates ratings and calculates weighted score.
   • Displays the most popular books across the dataset.

2. Content-Based Filtering:

   • Computes cosine similarity between book descriptions.
   • Finds books with high similarity scores to the input.

3. Pre-processed Data:

   • Uses .pkl files (saved models) for faster predictions.

git clone https://github.com/Arpit-Padmani/Book-Recommadation-System.git
cd book-recommendation-system

• Books.csv → Contains book details.

• Ratings.csv → Contains user ratings.

• Users.csv → User details (for dataset reference).

• app.py → Flask application entry point.

• books-recommandation-system.ipynb / news.ipynb → Jupyter notebooks for model building & experimentation.

• .pkl files → Precomputed models and data:

  • books.pkl → Processed books data
  • popular.pkl → Popularity-based recommendations
  • pt.pkl → Pivot table matrix
  • similarity_scores.pkl → Precomputed similarity scores

• templates/ (HTML templates for Flask UI):

  • index.html → Home page (search or landing page)
  • recommend.html → Displays recommended books

Run the Flask app:

python app.py

Then open your browser at:

http://127.0.0.1:5000/

• Programming Language: Python
• Data Handling: Pandas, NumPy
• Machine Learning: Scikit-Learn, Cosine Similarity
• Web Framework: Flask
• Visualization: Matplotlib, Seaborn
• Notebook Experiments: Jupyter

• If you like Harry Potter and the Sorcerer’s Stone → Get recommendations like Percy Jackson or The Hobbit.
• If you browse trending → See books like The Da Vinci Code or The Alchemist.

Dependencies are listed in requirements.txt:

Flask==2.3.2
pandas==2.0.3
numpy==1.24.3
scikit-learn==1.3.0
scipy==1.11.1
matplotlib==3.7.2
seaborn==0.12.2
joblib==1.3.1
notebook==7.0.2

Install them with:

pip install -r requirements.txt

Developed by Arpit Padmani 💼 LinkedIn