# 导入所需的库import numpy as npimport pandas as pdfrom sklearn.ensemble import RandomForestRegressorfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import mean_squared_error# 读取数据集data = pd.read_csv('shanghai_housing_price.csv')# 数据预处理data = data.drop(['Index', 'id', 'url', 'Cid', 'DOM'], axis=1) # 删除无关特征data = data.dropna() # 删除缺失值# 特征工程data['livingRoom'] = data['livingRoom'].apply(lambda x: int(x.split('室')[0]))data['drawingRoom'] = data['drawingRoom'].apply(lambda x: int(x.split('厅')[0]))data['kitchen'] = data['kitchen'].apply(lambda x: int(x.split('厨')[0]))data['bathroom'] = data['bathroom'].apply(lambda x: int(x.split('卫')[0]))data['constructionTime'] = data['constructionTime'].apply(lambda x: int(x.split('年')[0]))# 划分训练集和测试集X = data.iloc[:, :-1].valuesy = data.iloc[:, -1].valuesX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)# 训练模型rf = RandomForestRegressor(n_estimators=100, random_state=0)rf.fit(X_train, y_train)# 预测结果y_pred = rf.predict(X_test)# 评估模型mse = mean_squared_error(y_test, y_pred)print('均方误差：', mse)

要单独呈现csv文件，你可以在代码中使用pandas库中的to_csv()函数将数据保存为csv格式的文件，例如：

# 读取数据集
data = pd.read_csv('shanghai_housing_price.csv')

# 将数据保存为csv格式的文件
data.to_csv('shanghai_housing_price_clean.csv', index=False)

这样就可以将经过数据预处理和特征工程处理后的数据保存为一个新的csv文件，方便后续的使用。需要注意的是，to_csv()函数中的index参数表示是否保存行索引，默认为True，因此如果不需要保存行索引，需要将index参数设为False。

相关问题

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。

优化这段代码# import modules 导入模块 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

你可以对这段代码进行如下优化：

import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import colors
from matplotlib.colors import ListedColormap
from sklearn.model_selection import train_test_split, StratifiedShuffleSplit, StratifiedKFold, GridSearchCV, LeaveOneOut, cross_val_predict, KFold
from sklearn.decomposition import PCA
from sklearn.svm import SVC, LinearSVC
from sklearn.preprocessing import StandardScaler
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, StackingClassifier
from sklearn.metrics import roc_auc_score

# 导入模块

# 设置随机种子
random.seed(42)
np.random.seed(42)

# 其他代码...

这样做的优点是将导入的模块进行整理和分类，提高了代码的可读性。同时，设置了随机种子，保证了代码的可复现性。你可以根据需要添加其他的代码或模块。