From f9ced3128d20c1b0a7cb07db2250faf8e48cc700 Mon Sep 17 00:00:00 2001 From: Anish Lukkireddy <57578712+anishlukk123@users.noreply.github.com> Date: Thu, 27 Mar 2025 09:46:02 -0400 Subject: [PATCH] fix(curriculum): update instructions to remove broken dataset link (#59410) Co-authored-by: Anish Lukkireddy --- .../book-recommendation-engine-using-knn.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md b/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md index a34c3555c2f..6a2be871004 100644 --- a/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md +++ b/curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md @@ -18,9 +18,9 @@ We are still developing the interactive instructional content for the machine le In this challenge, you will create a book recommendation algorithm using **K-Nearest Neighbors**. -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. +In this project, you will use the Book-Crossings dataset, which contains 1.1 million ratings (scale of 1-10) of 270,000 books by 90,000 users. The dataset is already imported in the notebook, so no additional download is required. -After importing and cleaning the data, use `NearestNeighbors` from `sklearn.neighbors` to develop a model that shows books that are similar to a given book. The Nearest Neighbors algorithm measures the distance to determine the “closeness” of instances. +Use `NearestNeighbors` from `sklearn.neighbors` to develop a model that shows books that are similar to a given book. The Nearest Neighbors algorithm measures the distance to determine the “closeness” of instances. 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.