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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Book Recommender System\n\nA personalized book recommender system that suggests books to users based on their reading preferences and past ratings, using collaborative filtering techniques on the Book Crossing dataset.\n\n---\n\n## 📂 Project Structure\n```book-recommender-system/\n│\n├── Data/\n│ ├── Books.csv\n│ ├── Ratings.csv\n│ ├── Users.csv\n│\n├── Notebooks/\n│ ├── 01_data_preparation.ipynb\n│ ├── 02_book_recommendation_engine.ipynb\n│\n├── Outputs/\n│ ├── user_book_ratings.libsvm\n│ ├── Top5_Book_Recommendations.csv\n│\n├── Reports/\n│ ├── Book_Recommender_Report.docx\n│\n├── .gitignore\n└── README.md\n```\n\n---\n\n## 📘 Dataset Description\n\nThis project uses the [Book Crossing Dataset](https://www.kaggle.com/datasets/somnambwl/bookcrossing-dataset?resource=download) which includes:\n\n- **Books.csv** – Metadata such as title, author, and ISBN  \n- **Users.csv** – User IDs and demographics  \n- **Ratings.csv** – User ratings of books on a 0–10 scale  \n\nTotal:  \n📚 270,000+ books  \n👥 270,000+ users  \n⭐ 1 million+ ratings\n\n---\n\n## 🔍 Objective\n\nTo build a recommender system that:\n- Understands user preferences from prior ratings\n- Computes similarity between users using collaborative filtering\n- Recommends top 5 books that a user hasn’t read yet but is likely to enjoy\n\n---\n\n## ⚙️ Methodology\n\n1. **Data Cleaning \u0026 LIBSVM Preparation**  \n   [`01_data_preparation.ipynb`](Notebooks/01_data_preparation.ipynb)  \n   - Removes invalid entries  \n   - Filters non-positive ratings  \n   - Converts to sparse matrix and LIBSVM format  \n\n2. **Collaborative Filtering Engine**  \n   [`02_book_recommendation_engine.ipynb`](Notebooks/02_book_recommendation_engine.ipynb)  \n   - Builds user-user similarity matrix (Cosine similarity)  \n   - Finds top 10 nearest neighbors per user  \n   - Generates 5 book recommendations per user  \n   - Outputs results to CSV  \n\n---\n\n## 📁 Outputs\n\n- `user_book_ratings.libsvm`: Sparse matrix in LIBSVM format  \n- `Top5_Book_Recommendations.csv`: Final output with recommended books and scores  \n\n---\n\n## 🧾 Report\n\nA clean summary of project objectives, methodology, and outcomes is available here:  \n[`Book_Recommender_Report.docx`](Reports/Book_Recommender_Report.docx)\n\n---\n\n## 🧑‍💻 Technologies Used\n\n- Python (Pandas, NumPy, Scikit-learn, SciPy)\n- Jupyter Notebooks\n- Cosine Similarity (Collaborative Filtering)\n- LIBSVM Format\n\n---\n\n## 👨‍🎓 Course\n\nThis academic project was developed under the course **IFT 511: Analyzing Big Data (Spring 2025)** at **Arizona State University**.\n\n---\n\n## 📝 Authors\n\nTaljinder Singh 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