Ramicaxi/book-category-predictor

📚 Book Category Prediction Model

Welcome to the Book Category Prediction Model! This machine learning model uses a Random Forest Classifier to predict the category of a book based on its features. It has been trained on a Portuguese dataset containing book details like author, publisher, number of pages, and more.

🚀 Model Details

📖 Model Description

This Random Forest Classifier model is designed to predict the category of a book from its features, such as title, author, publisher, and additional attributes like number of pages and dimensions. The dataset is in Portuguese and includes a variety of book categories such as Management, HR, Literature, and many others.

• Developed by: Rami Aloui
• Model Type: Random Forest Classifier (ML Model)
• Primary Language: Portuguese (Dataset)
• License: MIT License

🔗 Model Sources

• Repository: Ramicaxi/book-category-predictor
  Access the full code and model files here!

🌍 Use Cases

🔹 Direct Use

This model can be used directly to predict a book's category based on its features. For example, you can input attributes like the number of pages or the book's dimensions, and the model will return a predicted category.

🔹 Downstream Use

The model can be fine-tuned on more specific book categories or integrated into larger recommendation systems for books, providing a more personalized experience for users.

🔹 Out-of-Scope Use

This model is primarily designed for book category prediction. It may not perform well on books that have significantly different attributes compared to those in the training dataset, or for tasks unrelated to book classification.

⚠️ Bias, Risks, and Limitations

As with any machine learning model, there are risks and limitations:

• Bias: This model may have biases due to the dataset being focused on Portuguese books. These biases could affect the predictions for books outside the dataset's scope.
• Data Limitations: The model may not generalize well to books that are very different from those included in the training dataset.

📝 Recommendations

• Always review the predictions for books that are not included in the dataset, as the model may not generalize well.
• Consider retraining the model with a more diverse and extensive dataset if you plan to use it for broader applications.

⚙️ How to Get Started with the Model

To get started, simply load the model and start making predictions. Below is an example of how to use the model:

import pickle
import pandas as pd

# Load the trained model
with open('book_category_model.pkl', 'rb') as f:
    model = pickle.load(f)

# Example: Create a dataframe for a new book
new_book = pd.DataFrame({
    'number_of_pages': [350],
    'dimension': [15.5],
    'other_feature': [value],  # Add additional features here
})

# Predict the category of the new book
predicted_category = model.predict(new_book)
print(f'Predicted Category: {predicted_category[0]}') 

🧠 How the Model Works

This model uses a Random Forest Classifier, a popular machine learning algorithm that operates by constructing multiple decision trees and combining their outputs to make predictions. The Random Forest model is particularly effective in handling classification tasks where the relationship between features and target labels is complex.

Key Features:

• Random Forest Classifier: A versatile and powerful machine learning model.
• Feature Importance: The model automatically ranks features (like book length, author, publisher) based on their importance in predicting book categories.
• Scalability: The model can handle large datasets with multiple features without significant performance degradation.

📊 Evaluation Metrics

The following metrics are used to evaluate the model's performance:

• Accuracy: Measures the overall correctness of the model.
• Confusion Matrix: Displays a matrix of actual vs. predicted categories, helping visualize the model’s performance across different categories.

💡 Future Enhancements

• Additional Features: Future versions of the model could integrate more features such as book genre, price, or publication date to improve prediction accuracy.
• Multi-language Support: Expanding the dataset and model to support other languages beyond Portuguese could significantly widen its use cases.
• Integration: The model could be integrated into a larger recommendation system for books or an e-commerce platform to suggest books based on user preferences.

⚠️ Limitations

• Bias: The model may be biased towards the types of books present in the training dataset. For example, the model is trained on a dataset of Portuguese books, so predictions for books in other languages or with different attributes may be less accurate.
• Data Quality: The accuracy of predictions is highly dependent on the quality and relevance of the input data. If the features are not well-defined or if new features are introduced, the model's performance might degrade.

📩 Feedback and Contributions

We welcome contributions and feedback! If you have any suggestions for improving the model or its implementation, feel free to create an issue or submit a pull request.

Created by: Rami Aloui