Recommender Systems with Machine Learning - Active Users and Popular Movies

The video tutorial explains how to count movies using data frames, calculate quantiles, filter movies based on popularity, manipulate data frames to drop unpopular movies, analyze user ratings, and set up collaborative filtering. It covers creating data frames, using group by functions, and applying thresholds to filter data. The tutorial also discusses calculating active users and setting up collaborative filtering for recommendation systems.

Describe how the shape of the ratings data changes after dropping popular movies.

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What are the steps to calculate the number of active users based on a rating threshold?

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What is collaborative filtering and how is it applied in this context?

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