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To start with, I will share the full code I used to perform the task of predicting the book's number of pages. Here is the code with comments. I have also included the parameters I used when running the code. I used Keras for most of the coding.
book_data = pd.read_csv('books.csv') # reading data into dataframe object book_data.head() ID Title Author Pages NumberofPages Rating Overall_rating Language # convert from string to int page_count_int = int(book_data['Pages'].str.replace(",", "")) book_data.drop(['Pages'], inplace=True) # drop the column page_count_int = book_data.drop(columns=['Pages']) # no need for it book_data.head() ID Title Author Overall_rating Language page_count_int # convert from string to int 1 THE LIFE OF CHARLES DICKENS 1 Charles Dickens 4.6 6 1 THE LIFE OF LOUIS XVI 1 Louis XVI 3.9 5 3 THE LIFE OF JOSEPH TROUBETZKOY 1 Joseph Troubetzkoy 4.7 8 4 THE LIFE OF JEAN JACQUES ROUSSEAU 1 Jean Jacques Rousseau 3.9 4 5 THE LIFE OF MICHELANGELO 1 Michelangelo 3.3 7 6 THE LIFE OF ALEXANDER PIERPONT DE TALLEYRAND 1 Alexander the First 3.9 1
## Convolutional neural network ## # import libraries from keras import Input, Conv2D, Conv2DTranspose, MaxPooling2D, Flatten, Dense # Importing the csv file from the above link using pandas library book_data = pd.read_csv('books.csv', delimiter=',') # read csv file and set the data frame as the input book_data.head() ID Title Author Overall_rating Language page_count_int 1 THE LIFE OF CHARLES DICKENS 1 Charles Dickens 4.6 6 1 THE LIFE OF LOUIS XVI 1 Louis XVI 3.9 5 3 THE LIFE OF JOSEPH TROUBETZKOY 1 Joseph Troubetzkoy 4.7 8 4 THE LIFE OF JEAN JACQUES ROUSSEAU 1 Jean Jacques Rousseau 3.9 4 5 THE LIFE OF MICHEL 3125841983
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