Fix MLflow model logging warnings

Added input_example parameter to auto-infer model signature and
explicitly set artifact_path parameter to remove deprecation warnings.

This improves MLflow tracking by:
- Auto-generating model signature from training data
- Using correct parameter names for MLflow 3.x
- Enabling better model serving and inference validation

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

src/models/train.py

@@ -137,7 +137,13 @@ def main():
 
             # Try to log model to MLflow (if permissions allow)
             try:
-                mlflow.sklearn.log_model(model, "model")
+                # Create input example for model signature
+                input_example = X_train.head(1)
+                mlflow.sklearn.log_model(
+                    model,
+                    artifact_path="model",
+                    input_example=input_example
+                )
                 print("\nModel logged to MLflow successfully!")
             except Exception as e:
                 print(f"\nWarning: Could not log model to MLflow: {e}")