Book Genre Prediction Project Guide

With millions of books being published globally, classifying them manually into genres like Mystery, Romance, Science Fiction, and Non-Fiction becomes tedious. Often, books come with only a description or synopsis, but no genre tag. Predicting the genre automatically based on a book's description can improve searchability, personalized recommendations, and user experience on book-selling platforms, libraries, and e-book services.

Using natural language processing and text classification algorithms, you can predict the genre of a book based solely on its description. Models like Logistic Regression, Random Forests, or fine-tuned BERT-based models can be trained on book description datasets. Semantic understanding plays a crucial role here — the model must identify subtle thematic elements and narrative styles hidden within the text to accurately predict genres across diverse literary works.

The system accepts a book description or summary as input, tokenizes and embeds the text into vectors, and feeds it into a classification model trained to map texts to genre labels. Preprocessing steps like removing stopwords, stemming, and n-gram creation help improve accuracy. Training on a multi-class dataset allows the model to predict from multiple genres like Romance, Horror, Mystery, Biography, and more, based purely on the narrative cues in the text.

• Collect datasets like Goodreads book descriptions, Amazon book data, or build a labeled dataset of books and their genres.
• Preprocess: clean and normalize the descriptions, tokenize, and create numerical feature representations using TF-IDF, Word2Vec, or Transformer embeddings.
• Train classifiers like Logistic Regression, Random Forest, or fine-tune BERT models on the description-to-genre classification task.
• Evaluate using multi-class classification metrics like accuracy, precision, recall, and confusion matrix analysis across genres.
• Deploy into a user interface where users can paste book summaries and get predicted genre suggestions instantly.

• Goodreads Dataset for Book Metadata
• Text Classification with scikit-learn
• Multi-Class Classification Using BERT