Amazon Books Genre Classifier

About The Project

This project aims to classify books into their main genres based on their titles or descriptions. It uses Machine Learning techniques, particularly a Support Vector Machine (SVM) model, to analyze book titles and predict their respective genres. The model was trained on a Amazon books dataset containing book Titles, Authors, Main Genre, Sub Genre and more attributes.

Features

• Dataset: The dataset can be found in the following link: here. It includes 3 different datasets: Books_df, Genre, and Sub_Genre.
  For this project we used Books_df dataset, which contains the following columns: Titles, Authors, Main Genre, Subgenre, Type, Price, Rating, No. of people voted, URLs. Subsequently, a transformation process was carried out to clean and prepare the data for analysis and model trainning.

• Data Analysis: Several visualizations were performed to gain insights into the dataset:

  • A plot showing the relationship between book ratings and the number of votes each book received per genre.
    
  • Analysis of average book prices across different genres, revealing pricing trends per genre.
    
  • Visualization of the top 10 authors with the most expensive books registerd by Amazon, highlighting high-value authors.
  • 
  • A scatter plot showing book price distribution by genre, providing an overview of price variability within each genre.
    

• Preprocessing: Text preprocessing includes lowering case, removing stop words, and using n-grams to capture meaningful patterns in the titles.

• Model: The core classifier is a trained SVM model that processes the titles or descriptions and predicts the most likely genre.

• Vectorization: The titles are converted into numerical vectors using TF-IDF (Term Frequency-Inverse Document Frequency) to represent their text-based features in a way the SVM model can process.

• Evaluation: The performance of the model was evaluated using metrics like F1-score and recall.

• Gradio Interface: An interactive interface was built using Gradio to allow users to input a book title and receive predictions about the book's most likely genre based on the trained model.

Getting started

To get running locally:

• Clone the repository

  git clone https://github.com/ALEXUSCR-27/Amazon-Books-Genre-Classifier.git

• Install all dependencies

  pip install -r requirements.txt

• Execute app.py file

  python app.py

• Access the interface with the local URL created by Gradio

  http://localhost:port

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