郑州市二手房房价预测python

根据提供的引用内容，以下是一个使用Python进行郑州市二手房房价预测的示例：

# 引入所需的库
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import GradientBoostingRegressor
import matplotlib.pyplot as plt

# 加载数据集
data = pd.read_csv('河南郑州二手房房价预测数据集.csv')

# 数据预处理
X = data.drop('房价', axis=1)
y = data['房价']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 数据标准化
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# 随机森林预测
rf = RandomForestRegressor()
rf.fit(X_train_scaled, y_train)
rf_predictions = rf.predict(X_test_scaled)

# SVM径向基核函数预测
svm = SVR(kernel='rbf')
svm.fit(X_train_scaled, y_train)
svm_predictions = svm.predict(X_test_scaled)

# KNN最邻近算法预测
knn = KNeighborsRegressor()
knn.fit(X_train_scaled, y_train)
knn_predictions = knn.predict(X_test_scaled)

# 决策树回归预测
dt = DecisionTreeRegressor()
dt.fit(X_train_scaled, y_train)
dt_predictions = dt.predict(X_test_scaled)

# 梯度提升决策分类预测
gb = GradientBoostingRegressor()
gb.fit(X_train_scaled, y_train)
gb_predictions = gb.predict(X_test_scaled)

# 可视化预测结果
plt.figure(figsize=(12, 6), facecolor='white')
plt.scatter(rf_predictions, y_test, marker='o', label='Random Forest')
plt.scatter(svm_predictions, y_test, marker='o', label='SVM')
plt.scatter(knn_predictions, y_test, marker='o', label='KNN')
plt.scatter(dt_predictions, y_test, marker='o', label='Decision Tree')
plt.scatter(gb_predictions, y_test, marker='o', label='Gradient Boosting')
plt.scatter(y_test, y_test, label='Actual')
plt.legend()
plt.show()

这个示例中，我们首先加载了数据集，然后进行了数据预处理，包括数据标准化和分割数据集。接下来，我们使用了随机森林、SVM、KNN、决策树回归和梯度提升决策分类等算法进行房价预测，并将预测结果可视化展示出来。