Recommender Systems with Machine Learning - Collaborative Filtering Using KNN

The video tutorial explains the KNN algorithm, its uses, and its drawbacks. It demonstrates how to merge and clean data frames using pandas, focusing on ISBN columns. The tutorial then shows how to group books by title and count their ratings, followed by merging the results for further analysis. Finally, it identifies popular books based on rating counts and sets the stage for filtering users by region in the next video.

What is a major drawback of the KNN algorithm?

• It becomes slow with large datasets.

• It requires a large amount of data.

• It is difficult to implement.

• It cannot be used for regression problems.

Which column is used to merge the 'ratings' and 'books' data frames?

• User ID

• ISBN

• Book Rating

• Book Title

Which columns are retained after merging the data frames?

• User ID, ISBN, Book Rating, Book Title

• User ID, ISBN, Author, Publisher

• User ID, Book Title, Author, Publisher

• ISBN, Book Rating, Author, Publisher

Which function is used to merge data frames in pandas?

• concat

• append

• join

• merge

What is the purpose of grouping the data by book titles?

• To find the most popular author

• To count the number of ratings for each book

• To identify books with missing data

• To calculate the average rating of each book

What function is used to remove rows with missing book titles?

• fillna

• drop_duplicates

• dropna

• replace

What does the 'describe' function provide about the data?

• Correlation between columns

• Visual representation of data

• List of unique values

• Summary statistics like mean and standard deviation

What is the threshold used to identify popular books?

• 100

• 25

• 50

• 10

What is the next step after identifying popular books?

• Calculating average ratings

• Merging with another data frame

• Filtering users by location

• Implementing KNN

What is the main focus of the final section of the video?

• Visualizing data

• Implementing a new algorithm

• Filtering data by user location

• Calculating book prices