基于SVM的预测波士顿房价

好的，我可以为你提供一个基于SVM的波士顿房价预测代码。首先，我们需要导入必要的库：

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error

然后，我们加载波士顿房价数据集，并将数据集划分为训练集和测试集：

boston_data = load_boston()
X = boston_data.data
y = boston_data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

接下来，我们对数据集进行标准化处理：

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

然后，我们使用支持向量回归（SVR）算法来训练模型：

clf = SVR(kernel='linear')
clf.fit(X_train, y_train)

最后，我们使用测试集来评估模型的均方误差：

y_pred = clf.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)

完整的代码如下：

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error

boston_data = load_boston()
X = boston_data.data
y = boston_data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

clf = SVR(kernel='linear')
clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)

这个代码可以用于预测波士顿房价，你也可以尝试使用其他数据集和算法。