导入各种库和包
fromsklearn.datasetsimportload_boston
fromsklearn.model_selectionimporttrain_test_split
fromsklearn.preprocessingimportStandardScaler
fromsklearn.linear_modelimportLinearRegression
fromsklearn.metricsimportmean_squared_error
importpandasaspd
importnumpyasnp
获取各种所需要的数据
data=load_boston()
data.keys()
导出横坐标的数据x
导出纵坐标的数据y
线性回归方程 完成机器学习六个步骤 1.导入数据 2.清洗数据 3.特征工程(提取有价值的数据)4.建模 5.评估 6.可视化(画图)
deflinear_model1():
data=load_boston()
print(data.data)
print(data.target)
x_train,x_test,y_train,y_test=train_test_split(data.data,data.target,random_state=22)
transfer=StandardScaler()
x_train=transfer.fit_transform(x_train)
x_test=transfer.fit_transform(x_test)
estimator=LinearRegression()
estimator.fit(x_train,y_train)
y_predict=estimator.predict(x_test)
print("预测值为:\n",y_predict)
print("模型中的系数为:\n",estimator.coef_)
print("模型中的偏置为:\n",estimator.intercept_)
error=mean_squared_error(y_test,y_predict)
print("误差为:\n",error)
returnNone
调用函数
学号:13430123
姓名:李军宏