Update app.py

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
+import seaborn as sns
 from sklearn.model_selection import train_test_split
 from sklearn.linear_model import LinearRegression
 from sklearn.tree import DecisionTreeClassifier
@@ -60,29 +61,39 @@ if uploaded_file is not None:
         # 排序
         feature_importance = feature_importance.sort_values('Random Forest', ascending=False)
 
+        # 繪製相關矩陣
+        st.write("### 相關矩陣")
+        corr_matrix = df.corr()
+        plt.figure(figsize=(10, 8))
+        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(model):
-            plt.figure(figsize=(10, 6))
-            plt.bar(feature_importance['Feature'], feature_importance[model])
-            plt.title(f'{model} Feature Importance')
+        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')
             plt.xlabel('Features')
             plt.ylabel('Importance')
-            plt.xticks(rotation=45, ha='right')
+            plt.xticks(indices, feature_importance['Feature'], rotation=45, ha='right')
+            plt.legend()
             st.pyplot(plt)
 
         # Streamlit UI
-        st.title("自定義CSV檔案分析 - 特徵重要性分析")
-        st.write("選擇一個模型來查看其特徵重要性：")
-
-        # 下拉選擇模型
-        model = st.selectbox("選擇模型", ["Linear Regression", "CART", "Random Forest"])
-
+        st.write("### 特徵重要性對比圖表 (Linear Regression, CART, Random Forest)")
+        
         # 顯示圖表
-        plot_importance(model)
+        plot_importance()
 
         # 顯示數據框
-        st.write(f"{model} 特徵重要性數據：")
-        st.dataframe(feature_importance[['Feature', model]])
+        st.write("### 特徵重要性數據表")
+        st.dataframe(feature_importance)
 
     else:
         st.error("上傳的檔案中找不到 'target' 欄位，請確認檔案格式。")