Content-Based Book Recommendation System 📚

An interactive Machine Learning-based recommendation system that suggests books based on cosine similarity of user choices and book metadata.

🚀 Features

• Cosine Similarity Matching: Implements text vectorization and cosine similarity calculations on book metadata (genre, author, description) to identify closest matches.
• Interactive Jupyter Notebook: Contains step-by-step exploratory data analysis (EDA), data cleaning, vectorization, and recommendation model building (Book_recommender.ipynb).
• Web Interface: A Flask-based web application allowing users to input a book and receive recommendations in real-time (app.py).

🛠️ Tech Stack

• Languages: Python
• Libraries: Pandas, NumPy, Scikit-learn (TfidfVectorizer, Cosine Similarity), Flask
• Frontend: HTML, CSS (integrated with dynamic templates)

📦 Getting Started

1. Install dependencies:

   pip install pandas numpy scikit-learn flask

2. Start the web application:

   python app.py

3. Open your browser and navigate to http://127.0.0.1:5000 to find your next favorite book!