Create README.md

README.md

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+---
+license: cc
+datasets:
+- jennifee/HW1-tabular-dataset
+language:
+- en
+metrics:
+- accuracy
+base_model:
+- autogluon/tabpfn-mix-1.0-classifier
+pipeline_tag: tabular-classification
+tags:
+- automl
+- classification
+- books
+- tabular
+- autogluon
+---
+
+# Model Card for AutoML Books Classification
+
+This model card documents the **AutoML Books Classification** model trained with **AutoGluon AutoML** on a classmate’s dataset of fiction and nonfiction books.  
+The task is to predict whether a book is **recommended to everyone** based on tabular features.  
+
+---
+
+## Model Details
+
+- **Developed by:** Bareethul Kader
+- **Framework:** AutoGluon (v1.1)  
+- **Repository:** [bareethul/AutoML-books-classification](https://huggingface.co/bareethul/AutoML-books-classification)  
+- **License:** CC BY 4.0  
+
+---
+
+## Intended Use
+
+### Direct Use
+- Educational demonstration of AutoML on a small tabular dataset.  
+- Comparison of multiple classical ML models through automated search.  
+- Understanding validation vs. test performance trade-offs.  
+
+### Out-of-Scope Use
+- Not designed for production or book recommendation engines.  
+- Dataset too small to generalize beyond classroom context.  
+
+---
+
+## Dataset
+
+- **Source:** https://huggingface.co/datasets/jennifee/HW1-tabular-dataset .  
+- **Task:** Classification (`RecommendToEveryone` = 0/1).  
+- **Size:** 30 original samples + ~300 augmented rows.  
+- **Features:**  
+  - `Pages` (integer)  
+  - `Thickness` (float)  
+  - `ReadStatus` (categorical: read/started/not read)  
+  - `Genre` (categorical: fiction/nonfiction)  
+  - `RecommendToEveryone` (binary target)  
+
+---
+
+## Training Setup
+
+- **AutoML framework:** AutoGluon TabularPredictor  
+- **Evaluation metric:** Accuracy  
+- **Budget:** ~1 minute training time, small scale search  
+- **Hardware:** Google Colab (T4 GPU not required, CPU sufficient)  
+- **Search Space:**  
+  - Tree based models: LightGBM, XGBoost, ExtraTrees, RandomForest  
+  - Neural nets: Torch, FastAI (small MLPs)  
+  - Bagging and ensembling across layers (L1, L2, L3)  
+
+---
+
+## Results
+
+### Mini Leaderboard (Top 3 Models)
+
+| Rank | Model                     | Test Accuracy | Validation Accuracy |
+|------|---------------------------|---------------|----------------------|
+| 1    | RandomForestEntr_BAG_L1   | **0.55**      | ~0.65               |
+| 2    | LightGBM_r96_BAG_L2       | 0.53          | ~0.72               |
+| 3    | LightGBMLarge_BAG_L2      | 0.53          | ~0.74               |
+
+- **Best model (AutoGluon selected):** `RandomForestEntr_BAG_L1`  
+- **Test Accuracy:** ~0.55  
+- **Validation Accuracy (best across runs):** up to ~0.75 (LightGBM variants)  
+
+Note: The **“best model”** may vary depending on random splits and seeds.  
+While AutoGluon reported `RandomForestEntr_BAG_L1` as best in this run, LightGBM models sometimes achieved higher validation accuracy but generalized less strongly.  
+
+---
+
+## Limitations, Biases, and Ethical Notes
+
+- **Small dataset size** → models may overfit, performance metrics unstable.  
+- **Augmented data** → synthetic rows may not reflect true variability.  
+- **Task scope** → purely educational, not for real world recommendation.  
+
+---
+
+## AI Usage Disclosure
+
+- ChatGPT (GPT-5) assisted in:  
+  - Helping with coding and AutoGluon AutoML approach on the go  
+  - Polishing the Colab notebook for clarity 
+  - Refining this model card  
+
+---
+
+## Citation
+
+**BibTeX:**  
+```bibtex
+@model{bareethul_books_classification,
+  author       = {Kader, Bareethul},
+  title        = {AutoML Books Classification},
+  year         = {2025},
+  framework    = {AutoGluon},
+  repository   = {https://huggingface.co/bareethul/AutoML-books-classification}
+}