Recommender Systems with Machine Learning - Age Distribution for Users

The video tutorial covers creating a histogram for user age distribution, analyzing the distribution, and exploring a ratings data frame. It demonstrates filtering the data based on user ID and book rating counts. Finally, it introduces using KNN for collaborative filtering, providing a comprehensive guide to data visualization and analysis techniques.

What is the purpose of setting up bins when creating a histogram?

• To save the figure in a specific format

• To label the axes of the graph

• To decide the range of data to be displayed

• To determine the color of the bars

In the age distribution histogram, which age group had the highest number of users?

• 25 years

• 40 years

• 75 years

• 100 years

What are the three columns present in the ratings data frame?

• User ID, ISP North, Book Rating

• User Name, ISP South, Movie Rating

• User Name, ISP North, Movie Rating

• User ID, ISP South, Book Rating

What is the threshold for filtering user IDs based on their count in the ratings data frame?

• 150

• 100

• 250

• 200

Which method is used to count the occurrences of user IDs in the ratings data frame?

• count_values()

• count_frequency()

• value_counts()

• frequency_count()

What is the minimum count required for book ratings to be included in the filtered data frame?

• 150

• 100

• 75

• 50

What is the next step after filtering the ratings data frame for collaborative filtering?

• Labeling the axes

• Saving the filtered data

• Creating a new histogram

• Using KNN for collaborative filtering