The main objective is to create a book recommendation system for users. Recommender systems are really critical in some industries as they can generate a huge amount of income when they are efficient or also be a way to stand out significantly from competitors.
- Descriptive Statistics
- Data Visualization
- Machine Learning
The Book-Crossing dataset comprises 3 files (Click here to access the Data)
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Users : Contains the users. Note that user IDs (User-ID) have been anonymized and map to integers. Demographic data is provided (Location, Age) if available. Otherwise, these fields contain NULL values.
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Books : Books are identified by their respective ISBN. Invalid ISBNs have already been removed from the dataset. Moreover, some content-based information is given (Book-Title, Book-Author, Year-Of-Publication, Publisher), obtained from Amazon Web Services. Note that in the case of several authors, only the first is provided. URLs linking to cover images are also given, appearing in three different flavors (Image-URL-S, Image-URL-M, Image-URL-L), i.e., small, medium, large. These URLs point to the Amazon website.
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Ratings : Contains the book rating information. Ratings (Book-Rating) are either explicit, expressed on a scale from 1-10 (higher values denoting higher appreciation), or implicit, expressed by 0.
- EDA - Performed exploratory data analysis on numerical and categorical data.
- Data Cleaning - Missing value imputation,Outlier Treatment
- Feature Selection - Used User-ID,ISBN and Books-Rating for model development.
- Model development - Tried Popularity based model and Collaborative filtering.
It is a type of recommendation system which works on the principle of popularity and or anything which is in trend. These systems check about the books which are in trend or are most popular among the users and directly recommend them.
Collaborative based filtering recommender systems are based on past interactions of users and target items. In simple words here, we try to search for the look-alike customers and offer products based on what his or her lookalike has chosen. Let us understand with an example. X and Y are two similar users and X user has watched A, B, and C movie. And Y user has watched B, C, and D movie then we will recommend A movie to Y user and D movie to X user.
Shivam Baldha, 'Introduction to Collaborative Filtering'. [Online].
Available: https://www.analyticsvidhya.com/blog/2022/02/introduction-to-collaborative-filtering/
Alexandre Wrg, 'An overview of several recommendation systems'. [Online].
Available: https://towardsdatascience.com/an-overview-of-several-recommendation-systems-f9f8afbf00ea/
Youtube.com, 'Book Recommender System'. [Online].
Available: https://www.youtube.com/watch?v=1YoD0fg3_EM&t=1s/
Deployed Project to this Webpage: https://book-recommendersystem.herokuapp.com/