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Book Recommendation Engine using KNN

You will be working on this project with Google Colaboratory.

After going to that link, create a copy of the notebook either in your own account or locally. Once you complete the project and it passes the test (included at that link), submit your project link below. If you are submitting a Google Colaboratory link, make sure to turn on link sharing for "anyone with the link."

We are still developing the interactive instructional content for the machine learning curriculum. For now, you can go through the video challenges in this certification. You may also have to seek out additional learning resources, similar to what you would do when working on a real-world project.

In this challenge, you will create a book recommendation algorithm using K-Nearest Neighbors (KNN).

Dataset

You will use the Book-Crossings dataset. This dataset contains 1.1 million ratings (scale of 1-10) of 270,000 books by 90,000 users.

Create a function

Create a function named get_recommends that takes a book title (from the dataset) as an argument and returns a list of 5 similar books with their distances from the book argument.

For example, the following code:

get_recommends("The Queen of the Damned (Vampire Chronicles (Paperback))")
[
  'The Queen of the Damned (Vampire Chronicles (Paperback))',
  [
    ['Catch 22', 0.793983519077301], 
    ['The Witching Hour (Lives of the Mayfair Witches)', 0.7448656558990479], 
    ['Interview with the Vampire', 0.7345068454742432],
    ['The Tale of the Body Thief (Vampire Chronicles (Paperback))', 0.5376338362693787],
    ['The Vampire Lestat (Vampire Chronicles, Book II)', 0.5178412199020386]
  ]
]