基于决策树算法对房价预测（包含数据集和代码）

好的，以下是基于决策树算法对房价预测的代码和数据集：

首先，我们需要准备数据集。此处使用的是scikit-learn库自带的波士顿房价数据集。

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
import pandas as pd

data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['price'] = data.target

X_train, X_test, y_train, y_test = train_test_split(df[data.feature_names], df['price'], test_size=0.2, random_state=42)

接着，我们使用sklearn库中的DecisionTreeRegressor类建立决策树模型，并训练模型：

from sklearn.tree import DecisionTreeRegressor

model = DecisionTreeRegressor(max_depth=5)
model.fit(X_train, y_train)

最后，我们使用测试集进行模型评估：

from sklearn.metrics import mean_squared_error

y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("MSE:", mse)

完整代码如下：

from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_error
import pandas as pd

# 准备数据集
data = load_boston()
df = pd.DataFrame(data.data, columns=data.feature_names)
df['price'] = data.target

X_train, X_test, y_train, y_test = train_test_split(df[data.feature_names], df['price'], test_size=0.2, random_state=42)

# 建立决策树模型并训练
model = DecisionTreeRegressor(max_depth=5)
model.fit(X_train, y_train)

# 模型评估
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print("MSE:", mse)

希望能对你有所帮助！