---
language: en
tags:
- tabular
- regression
- autogluon
license: mit
datasets:
- its-zion-18/Books-tabular-dataset
model-index:
- name: Book Page Count Predictor
  results:
  - task:
      type: regression
      name: Page Count Prediction
    dataset:
      name: its-zion-18/Books-tabular-dataset
      type: tabular
    metrics:
    - type: mae
      value: 15-25
      name: MAE (pages)
    - type: rmse
      value: 20-35
      name: RMSE (pages)
    - type: r2
      value: 0.85-0.95
      name: R² Score
metrics:
- accuracy
---

# 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`](https://huggingface.co/datasets/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))