Update app.py

app.py

@@ -68,28 +68,28 @@ if uploaded_file is not None:
         sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)
         st.pyplot(plt)
 
-        # 繪製特徵重要性圖表
-        def plot_importance():
-            plt.figure(figsize=(12, 8))
-            width = 0.25  # 條形圖寬度
-            indices = np.arange(len(feature_importance['Feature']))
-
-            plt.bar(indices - width, feature_importance['Linear Regression'], width=width, label='Linear Regression')
-            plt.bar(indices, feature_importance['CART'], width=width, label='CART')
-            plt.bar(indices + width, feature_importance['Random Forest'], width=width, label='Random Forest')
-
-            plt.title('Feature Importance Comparison Across Models')
+        # 分別繪製各個模型的特徵重要性圖表
+        def plot_individual_model(model_name):
+            plt.figure(figsize=(10, 6))
+            plt.bar(feature_importance['Feature'], feature_importance[model_name])
+            plt.title(f'{model_name} Feature Importance')
             plt.xlabel('Features')
             plt.ylabel('Importance')
-            plt.xticks(indices, feature_importance['Feature'], rotation=45, ha='right')
-            plt.legend()
+            plt.xticks(rotation=45, ha='right')
             st.pyplot(plt)
 
         # Streamlit UI
-        st.write("### 特徵重要性對比圖表 (Linear Regression, CART, Random Forest)")
-        
-        # 顯示圖表
-        plot_importance()
+        st.write("### 特徵重要性分析")
+
+        # 分開顯示三個模型的特徵重要性圖表
+        st.write("#### Linear Regression")
+        plot_individual_model('Linear Regression')
+
+        st.write("#### CART (Decision Tree)")
+        plot_individual_model('CART')
+
+        st.write("#### Random Forest")
+        plot_individual_model('Random Forest')
 
         # 顯示數據框
         st.write("### 特徵重要性數據表")