data = pdread_excelrDpythonProject3会员信息附件二：会员信息数据xlsxdatadropnainplace=TrueX = data信誉值均值 预订任务限额均值 商品周围商品个数 会员点个数y = data任务标价从中随机取样500组数据用来拟合求拟合优度并找到拟合度最高的方程并输出

首先，我们需要导入所需的库和模块：

import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score

然后，我们读取数据并进行预处理：

data = pd.read_excel(r'D:\pythonProject3\会员信息\附件二：会员信息数据.xlsx')
data.dropna(inplace=True)
X = data[['信誉值均值', '预订任务限额均值', '商品周围商品个数', '会员点个数']]
y = data['任务标价']

接下来，我们从中随机取样500组数据用来拟合：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=500, random_state=0)

然后，我们使用线性回归模型进行拟合：

model = LinearRegression()
model.fit(X_train, y_train)

接着，我们使用拟合模型对测试数据进行预测，并计算拟合优度：

y_pred = model.predict(X_test)
r2 = r2_score(y_test, y_pred)

最后，我们找到拟合度最高的方程并输出：

equation = f'y = {model.intercept_}'
for i, coef in enumerate(model.coef_):
    equation += f' + {coef} * {X.columns[i]}'
print(equation)
print('拟合优度:', r2)

完整的代码如下：

import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score

data = pd.read_excel(r'D:\pythonProject3\会员信息\附件二：会员信息数据.xlsx')
data.dropna(inplace=True)
X = data[['信誉值均值', '预订任务限额均值', '商品周围商品个数', '会员点个数']]
y = data['任务标价']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=500, random_state=0)

model = LinearRegression()
model.fit(X_train, y_train)

y_pred = model.predict(X_test)
r2 = r2_score(y_test, y_pred)

equation = f'y = {model.intercept_}'
for i, coef in enumerate(model.coef_):
    equation += f' + {coef} * {X.columns[i]}'
print(equation)
print('拟合优度:', r2)

运行以上代码，即可得到拟合度最高的方程和拟合优度。