In this assignment, you should work with
file. This file contains the detailed information about books scraped via the Goodreads . The dataset is downloaded from Kaggle website.
Each row in the file includes ten columns. Detailed description for each column is provided in the following:
bookID: A unique Identification number for each book.
title: The name under which the book was published.
authors: Names of the authors of the book. Multiple authors are delimited with -.
average_rating: The average rating of the book received in total.
isbn: Another unique number to identify the book, the International Standard Book Number.
isbn13: A 13-digit ISBN to identify the book, instead of the standard 11-digit ISBN.
language_code: Helps understand what is the primary language of the book.
num_pages: Number of pages the book contains.
ratings_count: Total number of ratings the book received.
text_reviews_count: Total number of written text reviews the book received.
Write the following codes:
Use pandas to read the file as a dataframe (named as books).bookIDcolumn should be the index of the dataframe.
Use books.head() to see the first 5 rows of the dataframe.
Use book.shape to find the number of rows and columns in the dataframe.
Use books.describe() to summarize the data.
Use books[‘authors’].describe() to find about number of unique authors in the dataset and also most frequent author.
Use OLS regression to test if average rating of a book is dependent to number of pages, number of ratings, and total number of written text reviews the book received.
Summarize your findings in part 1 (all 6 sections) in a Word file (you should include your code, and provide a summary that contains a summary of results such as number of rows in the dataset,
interpretation of regression results