基于神经网络的房价预测信息实现
时间: 2023-07-29 20:09:21 浏览: 88
【BP预测】基于BP神经网络实现房价预测附matlab代码 上传.zip
神经网络是一种非常强大的机器学习方法,可以用于房价预测。与线性回归模型相比,神经网络可以更好地处理非线性关系,提高预测的准确性。以下是一个基于Python语言的基于神经网络的房价预测的实现示例:
```python
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.utils import to_categorical
# 读取数据
data = pd.read_csv('house_price.csv')
# 数据预处理
data = data.dropna() # 去除缺失值
data = data[data['price'] > 0] # 去除异常值,价格必须大于0
data = data[data['room_num'] > 0] # 去除异常值,房间数必须大于0
# 特征选择
features = ['area', 'room_num', 'hall_num', 'floor', 'total_floor', 'year', 'district', 'subway']
data = pd.get_dummies(data, columns=['district', 'subway']) # 对分类变量进行独热编码
X = data[features]
y = data['price']
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# 模型建立
model = Sequential()
model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1, activation='linear'))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X_train, y_train, epochs=100, batch_size=32, validation_data=(X_test, y_test))
# 模型评估
mse = model.evaluate(X_test, y_test)
rmse = np.sqrt(mse)
print('均方误差:', mse)
print('均方根误差:', rmse)
# 模型应用
new_house = [[100, 2, 1, 8, 20, 2010, '浦东', '有']]
new_house = pd.DataFrame(new_house, columns=['area', 'room_num', 'hall_num', 'floor', 'total_floor', 'year', 'district', 'subway'])
new_house = pd.get_dummies(new_house, columns=['district', 'subway'])
predict_price = model.predict(new_house[features])
print('新房价预测值:', predict_price[0][0])
```
需要注意的是,神经网络的建立和训练需要较长时间,同时需要适当地调整神经网络的结构和参数,以达到最优的预测效果。
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