Book Recommendation Engine using KNN
This repository contains my solution approach from the FreeCodeCamp Machine Learning with Python Project - Book Recommendation Engine using KNN challenge. ( ⭐️ Star repo on GitHub — it helps! )
In this challenge, we 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.
You can access the full project instructions and starter code on Google Colaboratory.
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), "HAVE FUN WHILE SOLVING IT". If you are submitting a Google Colaboratory link, make sure to turn on link sharing for "anyone with the link."
Code
All code section are available directly and the detailed description of the data can be found in colab.
The project needs the following header files for the implementation:
- import numpy as np
- import pandas as pd
- from scipy.sparse import csr_matrix
- from sklearn.neighbors import NearestNeighbors
- import matplotlib.pyplot as plt
To run the project, do the following steps:
- Dowload the repo.
- Install all the above mentioned libraries.
python3 fcc_book_recommendation_knn.py- Check if following output is present in the end.
You passed the challenge! 🎉🎉🎉🎉🎉
Contributors
- Rohit Kumar Singh
Feedback
Feel free to send us feedback on file an issue. Feature requests are always welcome. If you wish to contribute, please take a quick look at this colab.
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