Question: Use the following datasets: BX-Book-Ratings.csv, BX-Books.csv, BX-Users.csv in this casestudy to develop right recommendation for users as it will entice the user to rent more books. You will get exposed to recommendation algorithms in this assignment.

Case Study

Domain - Retail

focus - Optimize Book RENT

Business challenge/requirement

BookRent is the largest online and offline book rental chain in India. The Company charges a fixed fee per month plus rental per book. So,the company makes more money when user rent more books.

You as an ML expert have to model recommendation engine so that user gets recommendation of books based on the behavior of similar users. This will ensure that users are renting books based on their individual taste.

Company is still unprofitable and is looking to improve both revenue and profit.

Key issues

As of now lot users return book and do not take new rental. Right recommendation

will entice user to rent more books

Considerations

NONE

Data volume

-Approx 1 M records

-file BX-Book-Ratings.csv and 2 more. But only 10K records will be used

Fields in Data

user_id: Unique Id of the User

isbn: International Standard Book Number is a unique numeric commercial book identifier

rating: rating given by user

Additional information

-NA

http://www.mediafire.com/file/s1b5gnfp13q8rau/BX_books_book_ratings_users.zip/file

Required datasets are in the link above.