🎯 Project Idea: Book Recommendation Engine (Using Goodreads or Kaggle Dataset)

🧩 Project Summary:

Build a recommendation engine that suggests books to users based on their past ratings and preferences. You’ll use real-world data, apply both collaborative and content-based filtering, and create an interactive recommendation interface.

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📁 Data Sources (Free & Public):

• Goodbooks-10k Dataset (Kaggle)
  • 10,000 books
  • 6 million user ratings
  • Includes titles, authors, genres, and user IDs

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🔨 What You’ll Build:

1. 📦 Data Preprocessing
   • Clean duplicates, nulls, and irrelevant metadata
   • Merge books.csv and ratings.csv
   • Optionally process genres or text descriptions
2. 🔍 Exploratory Data Analysis (EDA)
   • Most popular books/authors
   • Rating distribution
   • Sparsity analysis of user-item matrix
3. 📈 Modeling
   • Collaborative Filtering (SVD or KNN):
     • Recommend books based on user similarity or item similarity
   • Content-Based Filtering (TF-IDF or embeddings):
     • Recommend similar books based on genres, titles, or descriptions
   • Hybrid Recommendation:
     • Blend both approaches using weighted scores
4. 🧪 Evaluation
   • Use RMSE, Precision@K, Recall@K, Hit Rate
   • Visualize performance across models
5. 🖥️ Simple Web App (Optional but great for portfolio)
   • Use Streamlit, Flask, or Gradio
   • Allow a user to enter their favorite books or ratings
   • Display personalized recommendations

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🧠 Bonus Ideas to Add Complexity

• Implement cold-start logic for new users
• Add implicit feedback like clicks or book views
• Use word embeddings (Word2Vec or BERT) for better content similarity
• Create a re-ranking system based on popularity or recency

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🧳 Skills You’ll Practice

• Data wrangling and merging
• NLP and feature engineering
• Matrix factorization
• Similarity metrics
• Model evaluation
• Backend + dashboarding (if you build the app)

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