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books-page-count-predictor – AI Model by yusenthebot | AlphaNeural AI

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yusenthebot/
books-page-count-predictor

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Book Page Count Predictor

Model Details

Model Type: AutoGluon Tabular Predictor (Ensemble)
Task: Regression (Page Count Prediction)
Framework: AutoGluon 1.4.0
Training Data: Augmented book dimensions dataset
Input Features: Height, Width, Depth, Genre
Output: Predicted page count (integer)

Performance Metrics

MAE: ~15–25 pages (varies by test set)
RMSE: ~20–35 pages
R² Score: 0.85–0.95

Intended Use

Predict book page count from physical dimensions
Publishing industry applications
Library cataloging assistance
Book manufacturing planning

Limitations

Trained on limited genre categories (0, 1)
Assumes standard book formats
May not generalize to unusual book types (e.g., children’s board books, art books)

Training Details

Dataset: its-zion-18/Books-tabular-dataset
Train/Test Split: 80/20
Training Time: ~5 minutes (300s limit)
AutoGluon Preset: best_quality

Usage Example

Load Native AutoGluon Predictor

python

from autogluon.tabular import TabularPredictor

# Load the model (unzip first if using the .zip version)
predictor = TabularPredictor.load('path_to_model')

# Make prediction
sample = {'Height': 9.0, 'Width': 6.0, 'Depth': 1.0, 'Genre': 0}
prediction = predictor.predict(sample)
print("Predicted page count:", int(prediction))