Recommender Systems with Machine Learning - Item-Based Filtering Data Preparation

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, including handling CSV files, setting encodings, and defining columns. The tutorial proceeds to load user and ratings datasets, applying similar data preparation techniques. It further explores analyzing ratings data using matplotlib to visualize distributions. Finally, the video explores the books and users data, examining their structure and columns.

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

What is the name of the data set used in the analysis?

What command is used to read the CSV file for the books data set?

What encoding is specified for reading the data set?

How many columns are defined for the books data set?

What are the three main columns associated with the users data set?

What are the three columns defined for the ratings data set?

What is the command used to check the shape of the ratings data set?

What is the purpose of the command 'ratings.book_rating.value_counts()'?

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