Update pages/4_Model_Creation_and_Evaluation.py

pages/4_Model_Creation_and_Evaluation.py

@@ -207,7 +207,7 @@ if df is not None:
     )
 
     # Create the best model
-    st.markdown("## Create the Best Model")
+    st.markdown("## Create the Model with the best algorithm and parameters you have received by perfroming Hyperparameter Tuning using Optuna")
     st.markdown("## SVC(kernel='poly', gamma = 'scale', C = 974.1963187644974, degree = 2)")
     model = SVC(kernel='poly', gamma = 'scale', C = 974.1963187644974, degree = 2)
     st.write(model)
@@ -221,9 +221,9 @@ if df is not None:
     y_pred = model.predict(x_test_std)
     
     # Evaluation metrics
-    print("Accuracy:", accuracy_score(y_test, y_pred))
-    print("Classification Report:\n", classification_report(y_test, y_pred))
-    print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
+    st.write(print("Accuracy:", accuracy_score(y_test, y_pred)))
+    st.write(print("Classification Report:\n", classification_report(y_test, y_pred)))
+    st.write(print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred)))
 
     import streamlit as st
     import pandas as pd