International Journal of Academic Engineering Research (IJAER) ISSN: 2643-9085 Vol. 3 Issue 8, August – 2019, Pages: 1-5 www.ijeais.org/ijaer 1 ANN for Predicting Books' Overall Rating Ibrahim M. Nasser Department of Information Technology, Faculty of Engineering and Information Technology, Al-Azhar University, Gaza, Palestine Email: Azhar.ibrahimn@gmail.com, abunaser@alazhar.edu.ps Abstract: We developed an Artificial Neural Network (ANN) model for predicting the overall rating of books. The prediction is based on some Factors (bookID, title, authors, isbn, language_code, isbn13, # num_pages, ratings_count, text_reviews_count), which used as input variables and (average_rating) as output for our ANN predictive model. Our model established, trained, and validated using data set, which its title is "Goodreads-books". Model evaluation showed that the ANN model is able to predict correctly 99.90% of the validation instances. Keywords: Predictive Analysis, Data Mining, Machine Learning, Artificial Neural Networks, Books Rates, Goodreads 1. INTRODUCTION In this study, we will analyze the dataset which contains various information of the books on the website of the world's largest book archive and book proposal site GoodReads. This dataset also includes information on the names, writers and spelling languages of books, as well as the rating and total score based on the votes given by various users. Artificial neural networks (ANNs) will be used for the analysis. Artificial neural networks are like biological neural networks and offer a technique, which solves the problem of prediction [3]. Neural networks contain input, hidden and output layers. Hidden layers convert the input into usable thing to the output layer [5]. The ANN Model goes through training and validation on a dataset. Training in which that the network is trained is done on a dataset. Then a configuration is done to the weights of the connections between neurons. Validation in which that the network is validated to determine the prediction of a new dataset [6]. In this study, we used about 33% of the dataset instances for network validation, the remaining 67% for training. ANN Architecture is shown in figure 1. Figure 1: ANN Architecture 1. Literature Review Nasser et.al built neural network based models for the purpose of classification, prediction, and diagnosis. They proposed an ANN to predict the category of movie's rate [1], predict the price range of mobile phone [48], predict the category of animal [47], diagnosis the category of tumor [47], for diagnosis of Autism [49], and for diagnosis of lung cancer [40,46]. Authors in [12 – 26] developed ANN models that are very useful and valuable for classification, prediction, and diagnosis. International Journal of Academic Engineering Research (IJAER) ISSN: 2643-9085 Vol. 3 Issue 8, August – 2019, Pages: 1-5 www.ijeais.org/ijaer 2 2. Methodology We downloaded a data set from kaggle that contains books information from goodreads application/website. This dataset created by the user Soumik [4]. We did some preprocessing on the data, and then we trained our ANN model and validated it. 3. Original Dataset Description Table 1: Original Dataset Description # Attribute Description Type 1. bookID A unique Identification number for each book. Integer 2. title The name under which the book was published. String 3. authors Names of the authors of the book. Multiple authors are delimited with -. String 4. average_rating The average rating of the book received in total. Real 5. isbn Another unique number to identify the book, the International Standard Book Number. Long 6. language_code Helps understand what is the primary language of the book. For instance, eng is standard for English. String 7. isbn13 A 13-digit ISBN to identify the book, instead of the standard 11-digit ISBN. Long 8. # num_pages Number of pages the book contains. Integer 9. ratings_count Total number of ratings the book received. Integer 10. text_reviews_count Total number of written text reviews the book received. Integer 4. Dataset Preprocessing We wanted to use this dataset to a build an ANN model to predict the overall rating of the books (attribute number 4). The first thing we had to do, is choose a suitable factors for this prediction, and delete the unnecessary ones, we chose these factors to be our input to the predictive model: #num_pages, rating_count, text_reviews_count, language_code. Moreover, the dataset contain 13720 instances. After preprocessing it becomes 12241 which is a large a number to a neural network to deal with, so, we divided these samples to 8242 training instances, and 3999 validation instances. In addition, because of the integer numbers of the inputs are too large comparing with the real rate values, we did a normalization to them so all the data are real. Normalization formula was: )) ) )) . While checking the instances, it has been noticed that there are a conflict between some instances; which means, there at least two books with the same input values but different rates, we excluded for the secondary ones. Moreover, there were validation instances that are out of range, we converted them to training. Now, the dataset is ready for training and validation. 5. Our ANN Model The resulted predictive ANN model is shown in Figure 2 and Figure 5. International Journal of Academic Engineering Research (IJAER) ISSN: 2643-9085 Vol. 3 Issue 8, August – 2019, Pages: 1-5 www.ijeais.org/ijaer 3 Figure 2: Our ANN Model Validation Our ANN model was able to predict the books' overall rate with 99.90% accuracy, with about 0.008 errors as seen in figure (3). Furthermore, The Model showed that the most effective factor in a book's rate is the rating_count. More details are shown in figure (4). Figure 3: Validation and Errors International Journal of Academic Engineering Research (IJAER) ISSN: 2643-9085 Vol. 3 Issue 8, August – 2019, Pages: 1-5 www.ijeais.org/ijaer 4 Figure 4: Attributes Importance Figure 5: Details of our ANN Model 6. Conclusion A predictive Artificial Neural Network Model for predicting books' rating was developed. The Model trained and validated using a dataset from the goodreads application/ website. We did some preprocessing on the dataset to make it suitable as input to our ANN model. Validation showed that the model is 99.90% accurate. International Journal of Academic Engineering Research (IJAER) ISSN: 2643-9085 Vol. 3 Issue 8, August – 2019, Pages: 1-5 www.ijeais.org/ijaer 5 References 1. Abu-Naser, S. S. (2012). 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