Recommender Systems with Machine Learning - Missing Values

The video tutorial explains how to create a new data frame to handle missing data. It begins by introducing the concept of a missing data frame and proceeds to identify missing values using the 'is null' function. The tutorial demonstrates testing for null values and creating a new data frame to store these values. Finally, it concludes with a summary of the results and a preview of the next video, which will explore song genres.

What is the purpose of creating a new data frame named 'missing data frame'?

• To sort the data frame alphabetically

• To calculate the average of numerical columns

• To identify and handle missing values in a data frame

• To store all the songs in a playlist

Which function is used to check for null values in a data frame?

• is_missing

• is_blank

• is_null

• is_empty

What does the 'is null' function return when there are no null values in a column?

• Zero

• Null

• False

• True

How can you summarize the number of missing values in each column of a data frame?

• Using 'is null' followed by 'average'

• Using 'is null' followed by 'sum'

• Using 'is null' followed by 'count'

• Using 'is null' followed by 'max'

What is the result of using 'is null' followed by 'sum' on a data frame with no missing values?

• A list of column names with missing values

• A list of column names with zero values

• A list of column names with no missing values

• A list of column names with all values

What is the next step after identifying missing values in a data frame?

• Sort the data frame by missing values

• Ignore the missing values

• Create a new data frame to store missing values

• Delete the entire data frame

What conclusion is reached about the 'songs' data frame at the end of the process?

• It contains several missing values

• It has no missing values

• It needs to be reformatted

• It should be deleted