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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： “`python # 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。