Abstract:
Most people around the world are very fond of reading books offline and
online. Different people have different preferences when it comes to choosing
books and it has become important to find better books for readers. The awards
are one of the main criteria to select the best books. There are no existing
studies relevant to the same idea. Therefore, the purpose of this study is to
predict the best books such as award(s) winning books based on different ten
attributes such as author, average ratings, date, genre, language, pages,
publisher, ratings, reviews, and title. For this, a data set has been obtained
through online community platform called Kaggle. The dataset contains
information about books obtained from the ‘Goodread’ website. Those books
are related to the period between years 2000 and 2021. The dataset is preprocessed
by removing duplicate data, removing unnecessary special
characters and removing missing values etc. The Waikato Environment for
Knowledge Analysis (WEKA) data mining tool is used to rank the
preprocessed data, and hyper-parameter tuning was applied to enhance the
outcomes. Six machine learning methods at the classification level including
Random Forest, Support Vector Machine, Decision Trees, Multilayer
Perceptron, Logistic Regression, and Naive Bayes have been used to generate
prediction models. Using a Naive Bayes algorithm, we achieved 86.32%
higher accuracy with higher precision, recall, and f-measure values, as well as
the lowest error rates. The proposed method can predict the best books for
readers using the above attributes by the Naive Bayes algorithm successfully.
