Yashvj123 commited on
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8fa17eb
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1 Parent(s): 5a370ea

Update app.py

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Files changed (1) hide show
  1. app.py +10 -7
app.py CHANGED
@@ -399,6 +399,9 @@ elif st.session_state.current_page == "EDA":
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  # Model Building
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  elif st.session_state.current_page == "Model Building":
 
 
 
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  st.markdown("""
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  <h2 style='text-align: center; color: #333;'>Model Building</h2>
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  """, unsafe_allow_html=True)
@@ -456,19 +459,19 @@ elif st.session_state.current_page == "Model Building":
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  # Hyperparameter Tuning
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  st.markdown("""
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- <h2>⚡ Hyperparameter Tuning using Optuna</h2>
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  <p>We optimized hyperparameters for <b>KNN, Decision Tree, Bagging Regressor, and Random Forest</b> using <b>Optuna</b>.</p>
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  <p>Below are the <b>optimized parameters</b> for each model:</p>
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- <h5 style='color: #1363DF;'>🔹 K-Nearest Neighbors (KNN)</h5>
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  <ul>
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  <li><code>n_neighbors</code></li>
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- <li><code>p</code> (Distance metric)</li>
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  <li><code>weights</code></li>
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  <li><code>algorithm</code></li>
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  </ul>
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- <h5 style='color: #FF6D28;'>🔹 Decision Tree</h5>
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  <ul>
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  <li><code>max_depth</code></li>
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  <li><code>min_samples_split</code></li>
@@ -477,13 +480,13 @@ elif st.session_state.current_page == "Model Building":
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  <li><code>min_impurity_decrease</code></li>
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  </ul>
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- <h5 style='color: #2EB086;'>🔹 Bagging Regressor</h5>
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  <ul>
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  <li><code>n_estimators</code>: 10 to 50</li>
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  <li><code>max_samples</code>: 0.7 to 0.9</li>
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  </ul>
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- <h5 style='color: #8B5CF6;'>🔹 Random Forest</h5>
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  <ul>
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  <li><code>n_estimators</code>: 10 to 50</li>
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  <li><code>max_samples</code>: 0.7 to 0.9</li>
@@ -494,7 +497,7 @@ elif st.session_state.current_page == "Model Building":
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  # Model Performance Insights
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  st.markdown("""
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- <h2>📊 Model Performance Insights</h2>
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  <p>Here’s how our ensemble models performed on training and test datasets:</p>
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  """, unsafe_allow_html=True)
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  # Model Building
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  elif st.session_state.current_page == "Model Building":
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+
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+ st.markdown("<hr style='border:1px solid #ddd;'>", unsafe_allow_html=True)
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+
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  st.markdown("""
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  <h2 style='text-align: center; color: #333;'>Model Building</h2>
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  """, unsafe_allow_html=True)
 
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  # Hyperparameter Tuning
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  st.markdown("""
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+ <h2>Hyperparameter Tuning using Optuna ⚡</h2>
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  <p>We optimized hyperparameters for <b>KNN, Decision Tree, Bagging Regressor, and Random Forest</b> using <b>Optuna</b>.</p>
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  <p>Below are the <b>optimized parameters</b> for each model:</p>
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+ <h5>🔹 K-Nearest Neighbors (KNN)</h5>
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  <ul>
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  <li><code>n_neighbors</code></li>
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+ <li><code>p</code></li>
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  <li><code>weights</code></li>
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  <li><code>algorithm</code></li>
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  </ul>
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+ <h5>🔹 Decision Tree</h5>
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  <ul>
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  <li><code>max_depth</code></li>
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  <li><code>min_samples_split</code></li>
 
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  <li><code>min_impurity_decrease</code></li>
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  </ul>
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+ <h5>🔹 Bagging Regressor</h5>
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  <ul>
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  <li><code>n_estimators</code>: 10 to 50</li>
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  <li><code>max_samples</code>: 0.7 to 0.9</li>
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  </ul>
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+ <h5>🔹 Random Forest</h5>
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  <ul>
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  <li><code>n_estimators</code>: 10 to 50</li>
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  <li><code>max_samples</code>: 0.7 to 0.9</li>
 
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  # Model Performance Insights
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  st.markdown("""
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+ <h2>Model Performance Insights 📊</h2>
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  <p>Here’s how our ensemble models performed on training and test datasets:</p>
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  """, unsafe_allow_html=True)
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