Скачать или смотреть Book Recommender System – Build & Deploy Your ML Recommendation App | Full Walkthrough

In this video, we’ll dive deep into the Book Recommender System by AdilShamim8 — see the repo, the live demo, how it works end-to-end, and how you can build something similar to enhance your portfolio.

What you’ll learn:

Overview of the project’s features: personalized recommendations, content-based & collaborative filtering, user interface, etc.

Technologies used: Python, scikit-learn, Flask, Pandas, NumPy, the web frontend & deployment on Render

How the data is structured: user ratings, book metadata, genres/categories.

Walkthrough of the recommendation algorithms: how collaborative filtering works, how content-based filtering is implemented.

Demo of the live app: how users interact, how the system suggests similar books, UI aspects. (adil-book-recommender8.onrender.com)

Tips & possible improvements: hybrid methods, adding NLP (review text), user profiles, improving cold-start issues, etc.

Why this video helps:

You get a full project example you can clone, run, and customize

Great content for your portfolio & learning how recommendation systems are built and deployed

👉 Links:
GitHub Repository: https://github.com/AdilShamim8/Book-R...

Live Demo: https://adil-book-recommender8.onrend...

💬 Engage with the video:

Have you used recommendation systems before? Tell me which one (collaborative vs content-based) you like more!

If you try building one yourself, show me your version!

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