Recommender Systems with Machine Learning - Count

The video tutorial explains how to identify unique users and movies in a dataset using Python. It demonstrates calculating the number of unique users and movies, followed by grouping ratings to determine the count of each rating level. The tutorial concludes with a discussion on multiplying ratings by users and addressing unsatisfied numbers, setting the stage for further exploration in the next video.

What function is used to determine the number of unique users in a dataset?

• average()

• length()

• count()

• sum()

Which method is used to format strings for displaying unique user and movie counts?

• join()

• replace()

• format()

• split()

How is the dataset grouped to calculate the count of each rating level?

• By timestamp

• By rating column

• By user ID

• By movie ID

What is the purpose of grouping the dataset by the rating column?

• To sort the ratings

• To find the average rating

• To determine the size of each rating level

• To filter out low ratings

What is the significance of multiplying the number of users by the number of items?

• To calculate the total number of ratings

• To ensure all numbers are satisfied

• To visualize the data

• To find the average rating

What is the result of multiplying 610 by the number of items?

• A very big number

• The same number

• A small number

• Zero

What is the next step after ensuring all numbers are satisfied?

• Start a new dataset

• Give the remaining count a 0 rating

• Recalculate the ratings

• End the analysis