Update app.py
Browse files
app.py
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@@ -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)
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@@ -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
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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
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<ul>
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<li><code>n_neighbors</code></li>
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<li><code>p</code
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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
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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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@@ -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
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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
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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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@@ -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
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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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st.markdown("<hr style='border:1px solid #ddd;'>", unsafe_allow_html=True)
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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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