Update pages/4_Model Creation and Evaluation.py

pages/4_Model Creation and Evaluation.py

@@ -264,6 +264,14 @@ conf_matrix_svc
 """
 st.code(code_5, language='python')
 
+# Import necessary libraries
+import matplotlib.pyplot as plt  # Import for plotting
+import seaborn as sns  # Import for advanced visualizations
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix  # Metrics for evaluation
+
+# Step 5: Model Evaluation
+st.subheader("Step 5: Model Evaluation")
+
 # Calculate accuracy for SVC
 svc_accuracy = accuracy_score(y_test, y_pred_svc)
 
@@ -272,7 +280,7 @@ st.write(f"Accuracy for SVC: {svc_accuracy:.2f}")
 
 # Display classification report
 st.write("Classification Report for SVC:")
-st.text(classification_report(y_test, y_pred_svc))  # Use st.text for proper formatting
+st.text(classification_report(y_test, y_pred_svc))  # Use st.text for better text formatting in Streamlit
 
 # Compute confusion matrix
 conf_matrix_svc = confusion_matrix(y_test, y_pred_svc)
@@ -280,21 +288,20 @@ st.write("Confusion Matrix for SVC:")
 st.write(conf_matrix_svc)
 
 # Plot the confusion matrix using matplotlib and seaborn
-plt.figure(figsize=(8, 6))  # Adjust figure size
+plt.figure(figsize=(8, 6))  # Adjust figure size for better display
 sns.heatmap(conf_matrix_svc, annot=True, fmt="d", cmap="Blues", cbar=False, annot_kws={"size": 10})
 
-# Title and axis labels
+# Add titles and axis labels
 plt.title("Confusion Matrix for SVC", fontsize=14)
 plt.xlabel("Predicted Labels", fontsize=12)
 plt.ylabel("True Labels", fontsize=12)
 
-# Rotate tick labels for better readability
+# Rotate tick labels for readability
 plt.xticks(rotation=45, ha='right')
 plt.yticks(rotation=45, va='center')
 
 # Display the plot in Streamlit
 st.pyplot(plt)
-import streamlit as st
 
 # Custom CSS for buttons
 st.markdown(