Update README.md

README.md

@@ -54,6 +54,11 @@ This is an ensemble fraud detection system trained on 1.47M e-commerce transacti
 
 ### Usage
 
+```python
+### Usage
+
+## Warning: Need GPU environment with CUDA installed
+
 ```python
 import joblib
 import numpy as np
@@ -66,15 +71,18 @@ xgb_model = joblib.load("xgb_model.pkl")
 ensemble_model = joblib.load("ensemble_model.pkl")
 scaler = joblib.load("scaler.pkl")
 
-# Prepare your data (same features as training)
-X = ...  # Your transaction data
+# Prepare your data
+df = ...
 
-# Scale
-X_scaled = scaler.transform(X)
+X = df[df.columns.difference(['Is Fraudulent'])].copy()
+y = df['Is Fraudulent'].copy()
 
 # Predict with ensemble
-fraud_proba = ensemble_model.predict_proba(X_scaled)[:, 1]
-fraud_pred = ensemble_model.predict(X_scaled)
+fraud_proba = ensemble_model.predict_proba(X)[:, 1]
+fraud_pred = ensemble_model.predict(X)
+
+# Evaluate predictions
+evaluate_models([lr_model, rf_model, nn_model, xgb_model, ensemble_model], X, y, ['Logistic Regression', 'Random Forest', 'Neural Network', 'XGBoost', 'Stacking Ensemble'])
 ```
 
 ### License