import pandas as pd import numpy as np import winreg from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.linear_model import Ridge###导入岭回归算法 from sklearn.metrics import r2_score import winreg real_address = winreg.OpenKey(winreg.HKEY_CURRENT_USER,r'Software\Microsoft\Windows\CurrentVersion\Explorer\Shell Folders',) file_address=winreg.QueryValueEx(real_address, "Desktop")[0] file_address+='\\' file_origin=file_address+"\\源数据-分析\\D:\PC2\study python\salary.csv"#设立源数据文件的桌面绝对路径 house_price=pd.read_csv(file_origin)#https://www.kaggle.com/altavish/boston-housing-dataset house_price.isnull().sum() house_price1=house_price.dropna().reset_index() del house_price1["index"] house_price[house_price==0].count() train=house_price1.drop(["MEDV"],axis=1) X_train,X_test,y_train,y_test=train_test_split(train,house_price1["MEDV"],random_state=1) #将MEDV划分为预测值，其它的属性划分为特征值，并将数据划分成训练集和测试集。 ridge=Ridge(alpha=10)#确定约束参数 ridge.fit(X_train,y_train) print("岭回归训练模型得分："+str(r2_score(y_train,ridge.predict(X_train))))#训练集 print("岭回归待测模型得分："+str(r2_score(y_test,ridge.predict(X_test))))#待测集

这段代码是一个使用岭回归算法进行房价预测的示例。代码中先导入了所需的库，包括pandas、numpy、sklearn等。然后通过winreg库获取桌面路径并读取源数据文件（csv格式），对数据进行处理，包括处理缺失值和划分训练集和测试集。接下来使用Ridge函数创建岭回归模型，并通过fit函数对模型进行训练。最后输出训练集和测试集的R^2得分，评估模型的性能。

需要注意的是，代码中的文件路径和数据处理部分可能需要根据实际情况进行修改。另外，alpha参数是岭回归的超参数，用于控制正则化的程度。在代码中设置为10，你可以根据实际情况进行调整。

相关问题

import pandas as pd import numpy as np import winreg import sklearn from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.linear_model import Ridge###导入岭回归算法 from sklearn.metrics import r2_score import winreg

你已经成功导入了所需的库：pandas、numpy、winreg、sklearn以及sklearn中的LinearRegression、train_test_split、Ridge和r2_score。

现在你可以使用这些库中提供的功能来进行数据处理、建模和评估等任务。如果你需要进一步的帮助或有其他问题，请随时提问。

在Python中想运行from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random怎么操作

要在Python中运行这些代码，你需要确保已经安装了相应的包。大多数包都可以使用`pip`进行安装。

首先，在命令行中运行以下命令来安装所需的包：

pip install scikit-learn pandas matplotlib numpy

然后，在你的Python脚本中导入所需的库：

from sklearn.model_selection import train_test_split
from sklearn.decomposition import PCA
import pandas as pd
from sklearn import svm
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import colors
from sklearn.model_selection import train_test_split
from sklearn import datasets
from matplotlib.colors import ListedColormap
from sklearn.svm import SVC
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import StratifiedShuffleSplit, StratifiedKFold
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict
from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import roc_auc_score
import datetime
import multiprocessing as mp
from sklearn.ensemble import StackingClassifier
from sklearn.pipeline import make_pipeline
from sklearn.svm import LinearSVC

import random

请确保在运行这些代码之前，已经安装了所需的Python库。如果遇到任何问题，请确保已正确安装这些库，并且版本与代码兼容。