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+# Football Elite Classifier — AutoML (AutoGluon Tabular)
+
+**Task:** Binary classification — predict `Elite` (0/1) from tabular receiver stats.
+
+## Dataset
+- **Source:** <classmate name + HF dataset link if public>
+- **Split:** Stratified Train/Test = 80/20 on the **original** split.
+- **Features:** ['Tgt', 'Rec', 'Yds', 'YBC_per_R', 'YAC_per_R', 'ADOT', 'Drop_pct', 'Rat']
+- **Target:** `Elite` (0/1)
+- **Preprocessing:** Identifier columns dropped (e.g., Player). Numeric coercion; rows with NA removed.
+
+## Training (AutoML)
+- **Framework:** AutoGluon Tabular
+- **Preset:** `best_quality`
+- **Time budget:** 300s
+- **Seed:** 42
+- **Eval metric:** F1 (binary)
+- **Notes:** AutoGluon performs model selection, ensembling, and stacking automatically.
+
+## Results (Held-out Test)
+```json
+{
+  "accuracy": 0.8333333333333334,
+  "f1": 0.8
+}
+
+## Limitations & Ethics
+- Correlations do not imply causation; labels may reflect selection bias.
+- Out-of-distribution players/contexts may reduce performance.
+- Intended for coursework, not for real personnel decisions.
+
+## License
+- Code & weights: <MIT/Apache-2.0 or course-required license>
+
+## Acknowledgments
+AutoML with [AutoGluon Tabular].
+Trained in Google Colab.
+GenAI tools assisted with boilerplate and doc structure.
+James Kramers hugging face dataset