Update app.py

app.py

@@ -400,11 +400,11 @@ elif st.session_state.current_page == "EDA":
 # Model Building
 elif st.session_state.current_page == "Model Building":
     st.markdown("""
-    <h2 style='text-align: center; color: #333;'>🚀 Model Building</h2>
+    <h2 style='text-align: center; color: #333;'>Model Building</h2>
 """, unsafe_allow_html=True)
 
     st.markdown("""
-        <h4>📌 Introduction</h4>
+        <h2>Introduction</h2>
         <p>In this section, we explore different <b>Ensemble Learning</b> techniques to improve model performance.</p>
         <p>We implemented three ensemble models: 
         <span style='font-size:16px;'>🥇 <b>Voting Regressor</b> - 🎯 <b>Bagging Regressor</b> - 🌲 <b>Random Forest Regressor</b></span></p>
@@ -443,7 +443,7 @@ elif st.session_state.current_page == "Model Building":
     st.markdown("<hr style='border:1px solid #ddd;'>", unsafe_allow_html=True)
     
     st.markdown("""
-        <h4>⚖️ Combining High & Low Variance Models</h4>
+        <h2>Combining High & Low Variance Models</h2>
         <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>
@@ -456,7 +456,7 @@ elif st.session_state.current_page == "Model Building":
 
     # Hyperparameter Tuning
     st.markdown("""
-        <h4 style='color: #FF9F45;'>⚡ Hyperparameter Tuning using Optuna</h4>  
+        <h2>⚡ Hyperparameter Tuning using Optuna</h2>  
         <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>
         
@@ -494,7 +494,7 @@ elif st.session_state.current_page == "Model Building":
     
     # Model Performance Insights
     st.markdown("""
-        <h4 style='color: #00A19D;'>📊 Model Performance Insights</h4>  
+        <h2>📊 Model Performance Insights</h2>  
         <p>Here’s how our ensemble models performed on training and test datasets:</p>
     """, unsafe_allow_html=True)
     
@@ -549,7 +549,7 @@ elif st.session_state.current_page == "Model Building":
     
     # Choosing the Best Model
     st.markdown("""
-        <h4 style='color: #FF5D5D;'>🏆 Choosing the Best Model</h4>  
+        <h2>Choosing the Best Model</h2>  
         <ul>
             <li>🔍 We checked for <b>overfitting</b> (high training accuracy, low test accuracy).</li>
             <li>🚀 We avoided <b>underfitting</b> (low training and test accuracy).</li>