对糖尿病数据集进行回归分析，其中数据集取自skearn中的datasets，python

好的，针对糖尿病数据集进行回归分析可以按照以下步骤进行：

1. 导入必要的库和数据集

from sklearn.datasets import load_diabetes
import pandas as pd

diabetes = load_diabetes()
df = pd.DataFrame(data=diabetes.data, columns=diabetes.feature_names)
df['target'] = diabetes.target

2. 数据预处理

from sklearn.model_selection import train_test_split

X = df.drop('target', axis=1)
y = df['target']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

3. 模型训练

from sklearn.linear_model import LinearRegression

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

4. 模型评估

from sklearn.metrics import mean_squared_error, r2_score

y_pred = model.predict(X_test)

mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print('MSE: ', mse)
print('R2: ', r2)

以上就是糖尿病数据集回归分析的基本流程，你可以根据需要进行进一步的调参和优化。