Update app.py

app.py

@@ -399,13 +399,13 @@ elif st.session_state.current_page == "EDA":
 
 # Model Building
 elif st.session_state.current_page == "Model Building":
-
-    st.markdown("<hr style='border:1px solid #ddd;'>", unsafe_allow_html=True)
     
     st.markdown("""
     <h2 style='text-align: center; color: #333;'>Model Building</h2>
 """, unsafe_allow_html=True)
 
+    st.markdown("<hr style='border:1px solid #ddd;'>", unsafe_allow_html=True)
+
     st.markdown("""
         <h2>Introduction</h2>
         <p>In this section, we explore different <b>Ensemble Learning</b> techniques to improve model performance.</p>
@@ -446,7 +446,7 @@ elif st.session_state.current_page == "Model Building":
     st.markdown("<hr style='border:1px solid #ddd;'>", unsafe_allow_html=True)
     
     st.markdown("""
-        <h2>Combining High & Low Variance Models</h2>
+        <h3>Combining High & Low Variance Models</h3>
         <p>A crucial step to improve ensemble performance is <b>choosing models with different variance levels</b>:</p>
         <ul>
             <li><b>Voting Regressor:</b> Uses a combination of <b>high-variance</b> (Decision Tree, KNN with small K) and <b>low-variance</b> (KNN with large K, Decision Tree with depth constraint) models.</li>
@@ -459,7 +459,7 @@ elif st.session_state.current_page == "Model Building":
 
     # Hyperparameter Tuning
     st.markdown("""
-        <h2>Hyperparameter Tuning using Optuna ⚡</h2>  
+        <h3>Hyperparameter Tuning using Optuna ⚡</h3>  
         <p>We optimized hyperparameters for <b>KNN, Decision Tree, Bagging Regressor, and Random Forest</b> using <b>Optuna</b>.</p>
         <p>Below are the <b>optimized parameters</b> for each model:</p>
         
@@ -482,14 +482,14 @@ elif st.session_state.current_page == "Model Building":
     
         <h5>🔹 Bagging Regressor</h5>
         <ul>
-            <li><code>n_estimators</code>: 10 to 50</li>
-            <li><code>max_samples</code>: 0.7 to 0.9</li>
+            <li><code>n_estimators</code></li>
+            <li><code>max_samples</code></li>
         </ul>
     
         <h5>🔹 Random Forest</h5>
         <ul>
-            <li><code>n_estimators</code>: 10 to 50</li>
-            <li><code>max_samples</code>: 0.7 to 0.9</li>
+            <li><code>n_estimators</code></li>
+            <li><code>max_samples</code></li>
         </ul>
     """, unsafe_allow_html=True)
 
@@ -497,7 +497,7 @@ elif st.session_state.current_page == "Model Building":
     
     # Model Performance Insights
     st.markdown("""
-        <h2>Model Performance Insights 📊</h2>  
+        <h3>Model Performance Insights 📊</h3>  
         <p>Here’s how our ensemble models performed on training and test datasets:</p>
     """, unsafe_allow_html=True)
     
@@ -537,13 +537,13 @@ elif st.session_state.current_page == "Model Building":
                 <td>Bagging Ensemble</td>
                 <td>98.68%</td>
                 <td>95.04%</td>
-                <td>95.45%</td>
+                <td><b>95.45%</b></td>
             </tr>
             <tr>
                 <td>Random Forest</td>
                 <td>97.92%</td>
                 <td>94.71%</td>
-                <td><b>94.71%</b></td>
+                <td>94.71%</td>
             </tr>
         </table>
     """, unsafe_allow_html=True)
@@ -561,8 +561,10 @@ elif st.session_state.current_page == "Model Building":
         <p><b>✅ Final Choice:</b> <span style='color: #FF6D28;'>Bagging Ensemble</span> due to its strong generalization ability! 🚀</p>
     """, unsafe_allow_html=True)
 
+    if st.button("🔙 Go Back to Model Report"):
+        switch_page("Model Report")
+
 
-        
 # Hands-on Model Page
 elif st.session_state.current_page == "Hands-on Model":
     st.title("Hands-on Model")