Books Dataset Recommender System

Building a collaborative filtering recommender systems on books dataset

Dataset on kaggle

Contents

We have 3 dataframes and we do data cleaning and EDA for each

Data cleaning

• Removing unnecessary columns
• Renaming columns
• Check for NaN and duplicates
• convert types

Data understanding and EDA

• Different plots
• Data queries
• Relation between datasets

Data preprocessing

• Merging datasets
• Removing columns
• Removing NaN and duplicates
• Removing no rating values (zero)

Modeling

In this part we use collaborative filtering method to build a recommender system. We use both item-based and user-based.

• Item-based

  Here, we explore the relationship between the pair of items (the user who bought Y, also bought Z)

  Sample for Wild Animus book

• User-based

  Here, we look for the users who have rated various items in the same way

  Sample for a random user