Book Recommendation System

This code provides a system to recommend books based on user input. The dataset used is books.csv.

Python Libraries used

• numpy
• pandas
• seaborn
• matplotlib

Importing and Exploring the data

The data is imported from the books.csv file. It is then explored using the following methods:

df.isnull().sum()
df.info()
df.describe()

Data Exploration

The following visualizations are generated:

• Bar chart of top 10 books with the highest average rating
• Bar chart of top 10 authors with the most books
• Bar chart of top 10 books with the highest rating counts
• Distribution plot of average rating for all books
• Scatterplot of the relation between average rating and rating count
• Scatterplot of the relation between average rating and number of pages

Data Preparation

The data is prepared for the recommendation system using the following steps:

The feature matrix is constructed by one-hot encoding the rating, language, and adding average rating and rating count columns

The feature matrix is normalized using MinMaxScaler Building the Book Recommendation System

A nearest neighbors model is built using the feature matrix. The bookRecom function is used to recommend books based on user input.

Link

Google colab file is located at https://colab.research.google.com/drive/1PN64dRGQOQ3OQQwyfcUAxmIjSp78_qvW