1. 导入boston房价数据集
from sklearn.datasets import load_boston import pandas as pd boston = load_boston() df = pd.DataFrame(boston.data)
2. 一元线性回归模型，建立一个变量与房价之间的预测模型，并图形化显示。
from sklearn.linear_model import LinearRegression import matplotlib.pyplot as plt x = boston.data[:, 5] y = boston.target LinR = LinearRegression() LinR.fit(x.reshape(-1, 1), y) plt.figure(figsize=(10,6)) plt.scatter(x, y) w = LinR.coef_ b = LinR.intercept_ print(w, b) plt.plot(x, w * x + b, 'orange') plt.show()
系数与截距
3. 多元线性回归模型，建立13个变量与房价之间的预测模型，并检测模型好坏，并图形化显示检查结果。
from sklearn.datasets import load_boston
import pandas as pd
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
boston = load_boston()
df = pd.DataFrame(boston.data)
x_train, x_test, y_train, y_test = train_test_split(boston.data,boston.target,test_size=0.3)
lineR = LinearRegression()
lineR.fit(x_train,y_train)
import numpy as np
x_predict = lineR.predict(x_test)
# 打印预测的均方误差
print("预测的均方误差：", np.mean(x_predict - y_test)**2)
# 打印模型的分数
print("模型的分数：",lineR.score(x_test, y_test))
x = boston.data[:,12].reshape(-1,1)
y = boston.target
plt.figure(figsize=(10,6))
plt.scatter(x,y)
lineR = LinearRegression()
lineR.fit(x,y)
y_pred = lineR.predict(x)
plt.plot(x,y_pred,'r')
print(lineR.coef_,lineR.intercept_)
plt.show()
系数、截距
4. 一元多项式回归模型，建立一个变量与房价之间的预测模型，并图形化显示。
from sklearn.preprocessing import PolynomialFeatures poly = PolynomialFeatures(degree=3) x_poly = poly.fit_transform(x) print(x_poly) lrp = LinearRegression() lrp.fit(x_poly,y) y_poly_pred = lrp.predict(x_poly) plt.scatter(x,y) plt.scatter(x,y_pred) plt.scatter(x,y_poly_pred) plt.show()
模型参数
