Python for Machine Learning - The Complete Beginners Course - Data Pre-Processing

The video tutorial explains how to create a matrix with user IDs and movie titles to display user ratings. It uses the pivot_table function to structure the data, with user IDs as the index, movie titles as columns, and ratings as values. The tutorial highlights the presence of null values due to users not rating all movies and emphasizes the need for data preprocessing. Finally, it suggests sorting the most rated movies for better analysis.

What is the main challenge when creating a matrix with user IDs and movie titles?

• Too many users have rated all movies.

• Most users have not rated most movies, leading to many missing values.

• All users have rated all movies, leading to no missing values.

• There are no challenges in creating such a matrix.

Which function is used to create a matrix with user IDs as the index and movie titles as columns?

• pivot_table

• merge_table

• concat_table

• join_table

What does the 'head' method do when applied to the matrix?

• It removes null values from the matrix.

• It displays the first few rows of the matrix.

• It sorts the matrix by user IDs.

• It adds new columns to the matrix.

Why are there null values in the matrix?

• Because all users have rated all movies.

• Because most users have not seen or rated most movies.

• Because the data is corrupted.

• Because the matrix was not created correctly.

What is the next step after identifying null values in the data?

• Ignore the null values.

• Preprocess the data to clean it.

• Delete all rows with null values.

• Add random values to fill the nulls.