Recommender Systems Complete Course Beginner to Advanced - Machine Learning for Recommender Systems: Collaborative Filte

The video tutorial explains the KNN algorithm, its uses, and its drawbacks. It demonstrates how to merge two data frames, 'ratings' and 'books', using pandas, focusing on the ISBN column. The tutorial then shows how to clean the data by dropping unnecessary columns and handling missing values. It further explains how to group data by book titles to calculate total rating counts and merge these counts back into the main data frame. Finally, it discusses analyzing the data using the describe function and filtering popular books based on rating counts.

How do you calculate the total rating count for each book?

What steps are involved in merging the book rating count with the combined book rating?

What indicates that a book is popular based on the total rating count?

What is the significance of filtering users from specific regions?