Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import StandardScaler
+
+# 讀取數據
+df = pd.read_csv('heart.csv')
+
+# 準備特徵和目標變量
+X = df.drop('target', axis=1)
+y = df['target']
+
+# 分割數據
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# 標準化特徵
+scaler = StandardScaler()
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+# 計算特徵重要性
+def calculate_importance():
+    # Linear Regression
+    lr = LinearRegression()
+    lr.fit(X_train_scaled, y_train)
+    lr_importance = np.abs(lr.coef_)
+
+    # CART
+    cart = DecisionTreeClassifier(random_state=42)
+    cart.fit(X_train, y_train)
+    cart_importance = cart.feature_importances_
+
+    # Random Forest
+    rf = RandomForestClassifier(n_estimators=100, random_state=42)
+    rf.fit(X_train, y_train)
+    rf_importance = rf.feature_importances_
+
+    return lr_importance, cart_importance, rf_importance
+
+# 創建特徵重要性 DataFrame
+lr_importance, cart_importance, rf_importance = calculate_importance()
+feature_importance = pd.DataFrame({
+    'Feature': X.columns,
+    'Linear Regression': lr_importance,
+    'CART': cart_importance,
+    'Random Forest': rf_importance
+})
+
+# 排序
+feature_importance = feature_importance.sort_values('Random Forest', ascending=False)
+
+# 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')
+    plt.xlabel('Features')
+    plt.ylabel('Importance')
+    plt.xticks(rotation=45, ha='right')
+    st.pyplot(plt)
+
+# Streamlit UI
+st.title("心臟病預測模型特徵重要性分析")
+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"])
+
+# 顯示圖表
+plot_importance(model)
+
+# 顯示數據框
+st.write(f"{model} 特徵重要性數據：")
+st.dataframe(feature_importance[['Feature', model]])