Python for Machine Learning - The Complete Beginners Course - Sorting the Data by Correlation

The video tutorial explains how to sort movies by correlation to find similar ones, using Star Wars as an example. It highlights potential issues with the results due to movies watched only once by users who also watched Star Wars. To improve accuracy, it suggests filtering out movies with fewer than 100 reviews.

What is the primary focus of sorting movie data by correlation in this tutorial?

• To identify movies similar to 'Star Wars'

• To find the most popular movies overall

• To list movies with the highest number of reviews

• To determine the least watched movies

What is the expected outcome when the data frame is sorted by correlation?

• A list of movies with the most reviews

• A list of movies with the least correlation

• A list of movies similar to the selected movie

• A list of movies with the highest ratings

Why might some correlation results not make sense?

• Because the movies are from different genres

• Because the correlation algorithm is flawed

• Because some movies are only watched once by users who watched 'Star Wars'

• Because the data set is too small

What is the suggested solution to improve the accuracy of correlation results?

• Use a different correlation algorithm

• Focus only on popular movies

• Increase the number of movies in the data set

• Filter out movies with less than 100 reviews

What is the main reason for filtering movies with less than 100 reviews?

• To focus on movies from the same genre

• To reduce the size of the data set

• To ensure only well-reviewed movies are considered

• To eliminate movies that skew correlation results