Book Recommendation system using KNN
Step 1: Collect data on user ratings of books: This could be done by asking users to rate books on a scale of 1 to 5, or by collecting data on books that users have purchased or borrowed from libraries.
Step 2: Choose a similarity metric to measure the similarity between books: This could be done by counting the number of common authors, the number of common genres, or the average rating of the books.
Step 3: Choose a value for k: This is the number of most similar books that will be recommended to each user. A higher value of k will result in more recommendations, but it may also result in less accurate recommendations.
Step 4: Find the k most similar books to each user: This can be done using a variety of algorithms, such as the KNN algorithm.
Step 5: Recommend the books to the users: This can be done by displaying a list of the recommended books to the users, or by sending them emails with recommendations.
To run this code just copy the code or download the notebook and run it. The dataset link is already given in the notebook which will be downloaded during execution of the code.