Add README.md

README.md

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-
 ---
 library_name: autogluon
 pipeline_tag: tabular-classification
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   - en
 ---
 
-
 # Football Elite Classifier — AutoML (AutoGluon Tabular)
 
-**Task:** Binary classification — predict `Elite` (0/1) from tabular receiver stats.
+## Purpose
+This model was developed as part of a class assignment on designing and deploying AI/ML systems.  
+It demonstrates the use of AutoML (AutoGluon Tabular) to build a binary classifier on football 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.
+- **Source:** <classmate name + Hugging Face 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 applied; rows with NA removed.
+
+## Training Setup
+- **Framework:** AutoGluon Tabular  
+- **Preset:** `best_quality`  
+- **Time budget:** 300 seconds  
+- **Seed:** 42  
+- **Eval metric:** F1 (binary)  
+- **Hardware/Compute:** Colab CPU runtime (2 vCPUs, ~12 GB RAM)  
+- **AI Usage Disclosure:** Generative AI tools were used to help structure code and documentation; model training and results are real.
+
+## Hyperparameters / Search Space
+- AutoGluon explored LightGBM, XGBoost, and ensembling variants.  
+- Random state set for reproducibility.  
+- Auto-stacking and bagging enabled under `best_quality`.  
+- Internal hyperparameter tuning handled automatically by AutoGluon.
 
 ## Results (Held-out Test)
 ```json
@@ -39,6 +46,7 @@ language:
   "accuracy": 0.8333333333333334,
   "f1": 0.8
 }
+```
 
 
 ## Limitations & Ethics