波士顿房价预测python

好的，针对波士顿房价预测问题，可以使用Python进行建模和预测。以下是一个简单的实现过程：

1. 导入相关的库和数据集

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['target'] = boston.target

2. 数据预处理

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

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=42)

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

3. 建立模型

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential()
model.add(Dense(64, input_dim=13, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1, activation='linear'))

model.compile(loss='mean_squared_error', optimizer='adam')

4. 训练模型

model.fit(X_train, y_train, epochs=100, batch_size=16, verbose=1)

5. 预测结果

y_pred = model.predict(X_test)

6. 评估模型

from sklearn.metrics import mean_squared_error
mse = mean_squared_error(y_test, y_pred)
print("MSE: ", mse)

以上步骤是一个基本的波士顿房价预测模型的实现过程。当然，还有很多优化和调参的空间，比如使用交叉验证、调整模型参数等。