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# Model Card for aedupuga/recommendation_predictor 

### Model Description


This is an AutoGluon Tabular AutoML implementation on a tabular dataset recording different features of the book. The model predicts whether the author would 'Recommend' or 'Not Recommend' a book based on given features.


- **Model developed by:** Anuhya Edupuganti
- **Model type:** AutoGluon TabularPredictor


### Model Sources [optional]

<!-- Provide the basic links for the model. -->
- **Dataset:** jennifee/HW1-tabular-dataset


### Direct Use
- This model was intended to practice automl implementation on a tabular dataset

## Bias, Risks, and Limitations
- Small data size.
- Personal preference of the dataset creator in classification.

## Training Data:

The model was trained on the augmented split of the "jennifee/HW1-tabular-dataset" The data includes features such as FictionorNonfiction, NumPages, ThicknessInches, and ReadUnfinishedorUnread, with the target variable being RecommendtoEveryone (yes or no).

## Evaluation Data:

The model achieved an accuracy of 0.5000 and a weighted F1 score of 0.5212 on the original dataset.

## Model Card Contact

Anuhya Edupuganti (Carnegie Mellon Univerity)- aedupuga@andrew.cmu.edu