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I gathered the data from Kaggle. In this project, I focused on analyzing data and used many types of visualization such as univariate, bivariate, and multivariate. my answer support by visualization.

HTML 49.33% Jupyter Notebook 50.67%
python python3 pandas data-analysis amazon jupyter-notebook seaborn matplotlib visualization

analyze-amazon-top-50-bestselling-books-2009-2019's Introduction

Analyze-Amazon-Top-50-Bestselling-Books-2009-2019

Description of my data:

Dataset on Amazon's Top 50 best selling books from 2009 to 2019. Contains 550 books, data has been categorized into fiction and non-fiction using Goodreads.

the data contain 7 columns:

  • 1- Name: Name of the Book
  • 2- Author: The author of the Book
  • 3- User Rating: Amazon User Rating
  • 4- Reviews: Number of written reviews on amazon
  • 5- Price: The price of the book (As at 13/10/2020)
  • 6- Year: The Year(s) it ranked on the bestseller
  • 7- Genre: Whether fiction or non-fiction

summary of finding:

In my analysis I have tried to have the analysis through which it is possible to reveal the common characteristics of the best-selling books, but I have not reached a specific reason for making these books in the best-selling list. But I found some information; Like not all bestsellers have a high rating.

  • 1- It is not the most expensive books that are popular.
  • 2- The most expensive books are not the ones with the highest ratings and reviews.
  • 4- most common books are found in range 0 to 20 $.
  • 5- non- fiction books are the best seller than fiction books.
  • 6- the price of the books is less in last 3 year.

In my presentation:

I try to help place your book on the bestseller list by extrapolating the common characteristics from the bestsellers, such that the book is a cheap price and be non-fiction book.

you must download and then run presentation in jupter nootbook to work with you as a slideshow.

Eslam Abou-shashaa

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