Recommender Systems Complete Course Beginner to Advanced - Machine Learning for Recommender Systems: Item-Based Filterin

The video tutorial covers collaborative filtering, starting with importing necessary libraries like pandas, numpy, and matplotlib. It then demonstrates loading and preparing a book dataset, handling errors, and setting encodings. The tutorial proceeds to load user and ratings datasets, followed by analyzing and visualizing the ratings distribution using matplotlib. The video concludes with a brief look at the book and user data frames, setting the stage for further analysis in the next video.

What libraries are needed for the data analysis as mentioned in the text?

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What is the name of the dataset used for books?

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What encoding is used for reading the dataset?

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List the columns of the books dataset as described in the text.

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What are the three main columns associated with the users dataset?

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What is the purpose of the ratings dataset?

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What are the three columns in the ratings dataset?

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How do you check the shape of the ratings dataset?

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What type of plot is used to visualize the rating distribution?

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What is the next step after visualizing the ratings?

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