Data Scenario #8 - Recommendation System with NMF in Python : Book Recommendation Project

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📚 In this hands-on data science project, you’ll learn how to build a personalized book recommendation system using Non-negative Matrix Factorization (NMF) in Python.

You've just joined BookHive — an online bookstore looking to level up its recommendation engine. Using only user-book rating data, we'll walk through how to uncover hidden preferences and make personalized suggestions for every reader.

🔧 What You’ll Learn:
How recommendation systems work (collaborative filtering)

How to use NMF for dimensionality reduction
Creating and transforming a user-item matrix
Predicting missing ratings using matrix factorization
Evaluating model accuracy with RMSE
Tuning model performance by adjusting number of latent features

💻 Get the Code & Notebook
Follow along with the full notebook and all the project files here:
👉 GitHub Repository: https://github.com/ahmadvh/Data-Scenario

🛠 Tools & Libraries:
Python (pandas, numpy, matplotlib)
scikit-learn

🧠 Perfect For:
Data science beginners
Aspiring machine learning engineers
Anyone looking to add real-world projects to their portfolio

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