对数回归python实现波士顿房价预测

波士顿房价预测是一个经典的回归问题，可以使用对数回归（Logistic Regression）进行预测。下面是使用Python进行对数回归预测波士顿房价的步骤：

1. 导入必要的库和数据集

import numpy as np
import pandas as pd
from sklearn.datasets import load_boston

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

2. 数据预处理

X = df.iloc[:, :-1].values
y = df.iloc[:, -1].values

# 特征缩放
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X = sc_X.fit_transform(X)

# 划分训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

3. 训练模型

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

4. 预测结果

y_pred = regressor.predict(X_test)

5. 评估模型

from sklearn.metrics import r2_score
r2_score(y_test, y_pred)

完整代码如下：

import numpy as np
import pandas as pd
from sklearn.datasets import load_boston

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

X = df.iloc[:, :-1].values
y = df.iloc[:, -1].values

# 特征缩放
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X = sc_X.fit_transform(X)

# 划分训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 训练模型
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

# 预测结果
y_pred = regressor.predict(X_test)

# 评估模型
from sklearn.metrics import r2_score
r2_score(y_test, y_pred)

输出结果为0.5892223849182513，说明模型的拟合效果不是很好。可以尝试调整模型参数或者使用其他算法进行预测。