1. 导入boston房价数据集
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。
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。
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。
1 from sklearn.datasets import load_boston 2 import matplotlib.pyplot as plt 3 from sklearn.linear_model import LinearRegression 4 from sklearn.preprocessing import PolynomialFeatures 5 6 def yiyuan(data,x,y): 7 ‘‘‘一元模型并画图‘‘‘ 8 plt.scatter(x, y) 9 plt.plot(x, 9 * x - 30) 10 plt.show() 11 12 def duoyuan(data,x,y): 13 LineR = LinearRegression() 14 LineR.fit(x.reshape(-1, 1), y) 15 lr = LinearRegression() 16 lr.fit(data, y) 17 lr.coef_ #斜率 18 w = lr.coef_ 19 lr.intercept_#截距 20 b = lr.intercept_ 21 y_pred = LineR.predict(x) 22 return y_pred 23 24 25 def duoxiangsi(): 26 poly = PolynomialFeatures(degree=2) 27 x_poly = poly.fit_transform(x) 28 lp = LinearRegression() # G构建模型 29 lp.fit(x_poly, y) 30 y_poly_pred = lp.predict(x_poly) 31 32 plt.scatter(x, y) 33 plt.plot(x, y_poly_pred, ‘r‘) 34 plt.show() 35 36 lrp = LinearRegression() 37 lrp.fit(x_poly, y) 38 plt.scatter(x, y) 39 plt.scatter(x, y_pred) 40 plt.scatter(x, y_poly_pred) # 多项回归 41 plt.show() 42 43 44 45 46 47 48 if __name__ == ‘__main__‘: 49 boston = load_boston() 50 boston.keys() 51 data = boston.data 52 x = data[:, 5] 53 y = boston.target 54 y_pred = yiyuan(boston,x,y) 55 duoxiangsi()
原文地址:https://www.cnblogs.com/smallgrass/p/10122665.html
时间: 2024-10-08 14:35:11