jprdonnelly/freeCodeCamp
1
0
Fork 0
mirror of https://github.com/freeCodeCamp/freeCodeCamp.git synced 2026-04-26 07:00:55 -04:00
Code Issues Projects Releases Wiki Activity

fix(curriculum): update instructions to remove broken dataset link (#59410)

Browse Source 
Co-authored-by: Anish Lukkireddy <anishlukkireddy@Anishs-MacBook-Pro.local>
This commit is contained in:
Anish Lukkireddy
2025-03-27 09:46:02 -04:00
committed by GitHub
parent d6628979e9
commit f9ced3128d
1 changed files with 2 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/book-recommendation-engine-using-knn.md
View File

@@ -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 <a href="http://www2.informatik.uni-freiburg.de/~cziegler/BX/" target="_blank" rel="noopener noreferrer nofollow">Book-Crossings dataset</a>. 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.