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回归预测boston房价.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sat May 24 20:16:34 2025
+
+@author: LENOVO
+"""
+
+import numpy as np
+import pandas as pd 
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+from sklearn.datasets import load_boston
+boston = load_boston()
+#print(boston.data.shape,boston.target.shape,boston.feature_names)
+df = pd.DataFrame(boston.data,columns=boston.feature_names)
+df.columns.insert(-1,'PRICE')
+df["PRICE"] = boston.target
+#print(df)
+
+print("各种回归模型预测波士顿房价及区间:")
+print("注：性能以均方误差MSE来衡量 (除logistic regression)")
+print()
+
+print("1.多元线性回归:")
+from sklearn.linear_model import LinearRegression
+lr = LinearRegression()
+x = df.drop('PRICE',axis = 1)
+y = df['PRICE']
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25,
+random_state=33)
+lr.fit(x_train, y_train)
+y_pred = lr.predict(x_test)
+#print(lr.intercept_,lr.coef_)
+print("R²: {}".format(lr.score(x_test,y_test)))
+print("MSE: {}".format(round(mean_squared_error(y_pred,y_test),3)))
+print()
+
+print("2.多项式回归:")
+from sklearn.preprocessing import PolynomialFeatures
+for i in range(1,4):
+    poly_features = PolynomialFeatures(degree=i, include_bias=False)
+    x_train_poly = poly_features.fit_transform(x_train)
+    x_test_poly = poly_features.fit_transform(x_test)
+    lr.fit(x_train_poly,y_train)
+    y_pred = lr.predict(x_test_poly)
+    #print(lr.intercept_,lr.coef_)
+    print("R²: {}".format(lr.score(x_test_poly,y_test)))
+    print("MSE: {}".format(round(mean_squared_error(y_pred,y_test),3)))
+    print()
+
+print("3.逻辑回归:")
+import pandas as pd
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+y_train1 = pd.Series(y_train)
+y_test1 = pd.Series(y_test)
+y_train_binned = pd.cut(y_train1, bins=3, labels=['low', 'medium', 'high'])
+y_test_binned = pd.cut(y_test1, bins=3, labels=['low', 'medium', 'high'])
+log_reg = LogisticRegression(random_state=42)
+log_reg.fit(x_train, y_train_binned)
+y_pred_log = log_reg.predict(x_test)
+print("准确率:", round(accuracy_score(y_test_binned, y_pred_log),4))
+print()
+
+print("4.分位数回归:")
+import statsmodels.api as sm
+import statsmodels.formula.api as smf
+
+for q in [0.25,0.5,0.75]:
+    model = smf.quantreg('PRICE ~ RM + LSTAT', data=df)
+    result = model.fit(q=q)
+    #print(result.summary())  
+    pred = result.predict(x_test)
+    #print(f"预测分段{q}房价：", pred)
+    print(f"预测分段{q}房价 MSE:",round(mean_squared_error(pred,y_test),3))
+print()
+
+print("5.XGboost:")
+import xgboost as xgb
+data_dmatrix = xgb.DMatrix(data=x,label=y)
+xg_reg = xgb.XGBRegressor(objective ='reg:linear', colsample_bytree = 0.3, learning_rate = 0.1,max_depth = 5, alpha = 10, n_estimators = 10)
+xg_reg.fit(x_train,y_train)
+preds = xg_reg.predict(x_test)
+print("MSE: {}".format(round(mean_squared_error(preds,y_test),3)))