Comprehensive Literary Greats Dataset

50,000+ Books Rated and Awarded Across Language, Genre, and Format

By [source]

About this dataset

> This remarkable dataset provides an awe-inspiring collection of over 50,000 books, encompassing the world's best practices in literature, poetry, and authorship. For each book included in the dataset, users can gain access to a wealth of insightful information such as title, author(s), average rating given by readers and critics alike, a brief description highlighting its plot or characteristics; language it is written in; unique ISBN which enables potential buyers to locate their favorite works with ease; genres it belongs to; any awards it has won or characters that inhabit its storyworld. > > Additionally, seeking out readers' opinions on exceptional books is made easier due to the availability of bbeScore (best books ever score) alongside details for the most accurate ratings given through well-detailed breakdowns in “ratingsByStars” section. Making sure visibility and recognition are granted fairly – be it a classic novel from time immemorial or merely recently released newcomers - this source also allows us to evaluate new stories based off readers' engagement rate highlighted by likedPercent column (the percentage of readers who liked the book), bbeVotes (number of votes casted) as well as entries related to date published - including showstopping firstPublishDate! > > Aspiring literature researchers; literary historians and those seeking hidden literary gems alike would no doubt benefit from delving into this magnificent collection – 25 variables regarding different novels & poets that are presented by Kaggle open source dataset “Best Books Ever: A Comprehensive Historical Collection of Literary Greats”. What worlds awaits you?

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How to use the dataset

> Whether you are a student, researcher, or enthusiast of literature, this dataset provides a valuable source for exploring literary works from varied time periods and genres. By accessing all 25 variables in the dataset, readers have the opportunity to use them for building visualizations, creating new analysis tools and models, or finding books you might be interested in reading. > > First after downloading the dataset into Kaggle Notebooks platform or other programming interfaces of your choice such as R Studio/Python Jupyter Notebooks (Pandas) - make sure that data is arranged into columns with clearly labeled title names. This will help you understand which variable is related to what precise information. Afterwards explore each variable by finding any patterns across particular titles or interesting findings about certain authors/ratings that are available in your research interests. > > Utilize the vital columns of Title (title), Author(author), Rating (rating), Description (description), Language (language), Genres (genres) and Characters(characters) - these can assist you in discovering different trends between books according to style of composition or character types etc. Move further down on examining more specific details offered by Book Format(bookFormat), Edition(edition) Pages(pages). Peruse publisher info along with Publish Date(publishDate). Besides these structural elements also take note of Awards column considering recent recognition different titles have received; also observe how much ratings has been collected per text through Numbers Ratings column-(numRatings); analyze reader's feedback according on Ratings By Stars(_ratingsByStars); view LikedPercentage rate provided by readers when analyzing particular book(_likedPercent). > > Apart from more accessible factors mentioned previously delve deeper onto more sophisticated data presented: Setting (_setting); Cover Image (_coverImg); BbeScore_bbeScore); BbeVotes_bbeVotes). All those should provide greater insight when trying to explain why certain book has made its way onto GoodReads top selections list! To find value estimate test out Price (_price)) column too - determining if some texts retain large popularity despite rather costly publishing options cost-wise available on market currently? > > Finally combine different aspects observed while researching concerning individual titles- create personalized recommendations based upon released comprehensive lists! To achieve that utilize ISUBN code provided; compare publication Vs first publication dates historically recorded; verify awards labeling procedure relied upon give context information on discussed here books progress over years

Research Ideas

> - Creating a web or mobile application to enable users to find books by genre, author, setting, etc. Users would be able to search for books based on their favorite criteria and also see bbeScore and reviews from other users. > - Using machine learning algorithms such as clustering or topic modeling to analyze the text from book descriptions in order to better understand common themes in literature and literary eras throughout history. > - Developing a recommendation engine that takes into account the user's interests and favorites with book ratings and bbeVotes in order to provide more personalized recommendations of titles they might like

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > Data Source > >

License

> > > License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication > No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: books_1.Best_Books_Ever.csv

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > If you use this dataset in your research, please credit .