Update README.md

README.md

@@ -78,11 +78,11 @@ Mean  0.9313 ± 0.0029
 import joblib, catboost as cb
 from huggingface_hub import hf_hub_download
 
-lgb_model = joblib.load(hf_hub_download("AurelPx/IncomeSlayer-9000", "lgb_model.pkl"))
-xgb_model = joblib.load(hf_hub_download("AurelPx/IncomeSlayer-9000", "xgb_model.pkl"))
-encoder   = joblib.load(hf_hub_download("AurelPx/IncomeSlayer-9000", "ordinal_encoder.pkl"))
+lgb_model = joblib.load(hf_hub_download("AurelPx/BoostingEnsemble-Income-Classification", "lgb_model.pkl"))
+xgb_model = joblib.load(hf_hub_download("AurelPx/BoostingEnsemble-Income-Classification", "xgb_model.pkl"))
+encoder   = joblib.load(hf_hub_download("AurelPx/BoostingEnsemble-Income-Classification", "ordinal_encoder.pkl"))
 cb_model  = cb.CatBoostClassifier()
-cb_model.load_model(hf_hub_download("AurelPx/IncomeSlayer-9000", "cb_model.cbm"))
+cb_model.load_model(hf_hub_download("AurelPx/BoostingEnsemble-Income-Classification", "cb_model.cbm"))
 
 # Build X_enc (28 features) and X_cb_df (21 cols, native categoricals) — see train.py
 proba  = 0.1 * lgb_model.predict_proba(X_enc)[:, 1] \
@@ -115,6 +115,6 @@ Full preprocessing pipeline in `train.py`.
   author = {AurelPx},
   title  = {Stacked GBM Ensemble for Income Classification (OpenML Task 7592)},
   year   = {2026},
-  url    = {https://huggingface.co/AurelPx/IncomeSlayer-9000}
+  url    = {https://huggingface.co/AurelPx/BoostingEnsemble-Income-Classification}
 }
 ```