Update app.py
Browse files
app.py
CHANGED
@@ -2,6 +2,7 @@ import streamlit as st
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LinearRegression
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from sklearn.tree import DecisionTreeClassifier
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@@ -60,29 +61,39 @@ if uploaded_file is not None:
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# 排序
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feature_importance = feature_importance.sort_values('Random Forest', ascending=False)
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# 繪製特徵重要性圖表
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def plot_importance(
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plt.figure(figsize=(
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plt.xlabel('Features')
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plt.ylabel('Importance')
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plt.xticks(rotation=45, ha='right')
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st.pyplot(plt)
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# Streamlit UI
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st.
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# 下拉選擇模型
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model = st.selectbox("選擇模型", ["Linear Regression", "CART", "Random Forest"])
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# 顯示圖表
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plot_importance(
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# 顯示數據框
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st.write(
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st.dataframe(feature_importance
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else:
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st.error("上傳的檔案中找不到 'target' 欄位,請確認檔案格式。")
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import seaborn as sns
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LinearRegression
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from sklearn.tree import DecisionTreeClassifier
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# 排序
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feature_importance = feature_importance.sort_values('Random Forest', ascending=False)
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# 繪製相關矩陣
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st.write("### 相關矩陣")
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corr_matrix = df.corr()
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plt.figure(figsize=(10, 8))
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sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)
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st.pyplot(plt)
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# 繪製特徵重要性圖表
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def plot_importance():
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plt.figure(figsize=(12, 8))
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width = 0.25 # 條形圖寬度
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indices = np.arange(len(feature_importance['Feature']))
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plt.bar(indices - width, feature_importance['Linear Regression'], width=width, label='Linear Regression')
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plt.bar(indices, feature_importance['CART'], width=width, label='CART')
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plt.bar(indices + width, feature_importance['Random Forest'], width=width, label='Random Forest')
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plt.title('Feature Importance Comparison Across Models')
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plt.xlabel('Features')
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plt.ylabel('Importance')
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plt.xticks(indices, feature_importance['Feature'], rotation=45, ha='right')
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plt.legend()
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st.pyplot(plt)
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# Streamlit UI
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st.write("### 特徵重要性對比圖表 (Linear Regression, CART, Random Forest)")
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# 顯示圖表
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plot_importance()
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# 顯示數據框
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st.write("### 特徵重要性數據表")
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st.dataframe(feature_importance)
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else:
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st.error("上傳的檔案中找不到 'target' 欄位,請確認檔案格式。")
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