Update README.md

README.md

@@ -148,6 +148,8 @@ with open("best_classifier.pkl", "rb") as f:
 prediction = model.predict(X_sample)
 
 
+---
+
 
 ## Conclusion
 
@@ -168,4 +170,4 @@ The work demonstrates a full machine learning workflow, including:
 - Model selection
 - Export
 
-The final Random Forest model delivers strong performance and can be used to classify athletes into strength categories based on their physical and strength metrics.
+The final Random Forest model delivers strong performance and can be used to classify athletes into strength categories based on their physical and strength metrics.